osciloskop-hystogram-i-raw.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots of oscilloscope records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "\n",
    "for f in glob.iglob(\"./data/data*.h5\"): # generator, search immediate subdirectories \n",
    "    #print '  ', f\n",
    "    #break\n",
    "\n",
    "#with h5py.File('./data/data01_*.h5','r') as hf:\n",
    "    with h5py.File(f,'r') as hf:\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        '''\n",
    "        print 'NORMAL'\n",
    "        print '  XINC:', np.array(hf.get('XINC')), '   YINC:', np.array(hf.get('YINC')), \n",
    "        print '   FRAMES:', np.array(hf.get('FRAMES')), '   TRIG:', np.array(hf.get('TRIG')), \n",
    "        print '   XORIGIN:', np.array(hf.get('XORIGIN'))\n",
    "        print '   YORIGIN:', np.array(hf.get('YORIGIN'))\n",
    "        print '   TIME:   ', np.array(hf.get('TIME'))\n",
    "\n",
    "        print 'RAW'\n",
    "        print '  XINC:', np.array(hf.get('XINCR')), '   YINC:', np.array(hf.get('YINCR')), \n",
    "        print '   FRAMES:', np.array(hf.get('FRAMES')), '   TRIG:', np.array(hf.get('TRIGR')), \n",
    "        print '   XORIGIN:', np.array(hf.get('XORIGINR'))\n",
    "        print '   YORIGIN:', np.array(hf.get('YORIGINR'))\n",
    "        print '   TIME:   ', np.array(hf.get('TIME'))\n",
    "        '''\n",
    "\n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            np_data = 1.0 * np.array(data)\n",
    "            #print 'np_data:', max(np_data)\n",
    "            np_data = np_data - 128 - np.array(hf.get('YORIGIN'))\n",
    "            np_data = np.array(hf.get('YINC')) * np_data * 1000  # to mV\n",
    "            #np_data -= 5\n",
    "            np_time = range(0,1400)\n",
    "            np_time *= np.array(hf.get('XINC'))\n",
    "            np_time *= 1e6   # to microseconds\n",
    "            np_time -= 10    # trigger time offset\n",
    "            np_time += 1.0e6*np.array(hf.get('XORIGIN'))\n",
    "            np_time *= 1.0e-3 # milliseconds are more fun\n",
    "\n",
    "            plt.figure(figsize=(18,5))\n",
    "            plt.plot(np_time, np_data)\n",
    "            plt.title(f)\n",
    "            plt.xlabel('Time [us]')\n",
    "            plt.ylabel('Amplitude [V]')\n",
    "\n",
    "            # Process the raw data\n",
    "            rawdata = hf.get(str(n)+'R')\n",
    "            np_rawdata = 1.0 * np.array(rawdata)\n",
    "\n",
    "            np_rawdata = np_rawdata - 128 - np.array(hf.get('YORIGINR'))\n",
    "            np_rawdata = np.array(hf.get('YINCR')) * np_rawdata    # to Volts\n",
    "            np_rawtime = range(0,len(np_rawdata))\n",
    "            np_rawtime *= np.array(hf.get('XINCR'))\n",
    "            np_rawtime *= 1e6   # to microseconds\n",
    "            np_rawtime -= 10    # trigger time offset\n",
    "            np_rawtime += 1.0e6*np.array(hf.get('XORIGINR'))\n",
    "            np_rawtime *= 1.0e-3 # milliseconds are more fun\n",
    "\n",
    "            #plt.figure(figsize=(18,5))\n",
    "            #plt.plot(np_rawtime,np_rawdata)\n",
    "\n",
    "            plt.xlabel('Time [us]')\n",
    "            plt.ylabel('Amplitude [mV]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read waveforms from the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ./data/data12_1563032432-CHAN1.h5\n",
      "NORMAL\n",
      "  XINC: 2e-06    YINC: 0.0008    FRAMES: 1    TRIG: 0.02008    XORIGIN: -0.0014\n",
      "   YORIGIN: 0.0\n",
      "   TIME:    None\n",
      "RAW\n",
      "  XINC: 2e-09    YINC: 0.0008    FRAMES: 1    TRIG: 0.02008    XORIGIN: -0.0014\n",
      "   YORIGIN: 0.0\n",
      "   TIME:    None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Amplitude [V]')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [18, 5]\n",
    "\n",
    "for f in glob.iglob(\"./data/data12_*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    break\n",
    "\n",
    "#with h5py.File('./data/data01_*.h5','r') as hf:\n",
    "with h5py.File(f,'r') as hf:\n",
    "    #print('List of arrays in this file: \\n', hf.keys())\n",
    "    print 'NORMAL'\n",
    "    print '  XINC:', np.array(hf.get('XINC')), '   YINC:', np.array(hf.get('YINC')), \n",
    "    print '   FRAMES:', np.array(hf.get('FRAMES')), '   TRIG:', np.array(hf.get('TRIG')), \n",
    "    print '   XORIGIN:', np.array(hf.get('XORIGIN'))\n",
    "    print '   YORIGIN:', np.array(hf.get('YORIGIN'))\n",
    "    print '   TIME:   ', np.array(hf.get('TIME'))\n",
    "\n",
    "    print 'RAW'\n",
    "    print '  XINC:', np.array(hf.get('XINCR')), '   YINC:', np.array(hf.get('YINCR')), \n",
    "    print '   FRAMES:', np.array(hf.get('FRAMES')), '   TRIG:', np.array(hf.get('TRIGR')), \n",
    "    print '   XORIGIN:', np.array(hf.get('XORIGINR'))\n",
    "    print '   YORIGIN:', np.array(hf.get('YORIGINR'))\n",
    "    print '   TIME:   ', np.array(hf.get('TIME'))\n",
    "\n",
    "    \n",
    "    frames = np.array(hf.get('FRAMES'))\n",
    "    for n in range(1,frames+1):\n",
    "        data = hf.get(str(n))\n",
    "        np_data = 1.0 * np.array(data)\n",
    "        #print 'np_data:', max(np_data)\n",
    "        np_data = np_data - 128 - np.array(hf.get('YORIGIN'))\n",
    "        np_data = np.array(hf.get('YINC')) * np_data    # to Volts\n",
    "        np_data -= 5\n",
    "        np_time = range(0,1400)\n",
    "        np_time *= np.array(hf.get('XINC'))\n",
    "        np_time *= 1e6   # to microseconds\n",
    "        np_time -= 10    # trigger time offset\n",
    "        np_time += 1.0e6*np.array(hf.get('XORIGIN'))\n",
    "        np_time *= 1.0e-3 # milliseconds are more fun\n",
    "                \n",
    "        #plt.plot(np_time, np_data)\n",
    "\n",
    "        # Process the raw data\n",
    "        rawdata = hf.get(str(n)+'R')\n",
    "        np_rawdata = 1.0 * np.array(rawdata)\n",
    "\n",
    "        np_rawdata = np_rawdata - 128 - np.array(hf.get('YORIGINR'))\n",
    "        np_rawdata = np.array(hf.get('YINCR')) * np_rawdata    # to Volts\n",
    "        np_rawtime = range(0,len(np_rawdata))\n",
    "        np_rawtime *= np.array(hf.get('XINCR'))\n",
    "        np_rawtime *= 1e6   # to microseconds\n",
    "        np_rawtime -= 10    # trigger time offset\n",
    "        np_rawtime += 1.0e6*np.array(hf.get('XORIGINR'))\n",
    "        np_rawtime *= 1.0e-3 # milliseconds are more fun\n",
    "        plt.plot(np_rawtime,np_rawdata)\n",
    "        \n",
    "plt.xlabel('Time [us]')\n",
    "plt.ylabel('Amplitude [V]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse all data files in current directory\n",
    "Read in all the data files in current directory and process them into a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ./compare/data01_1548186723-CHAN1.h5\n",
      "XINC: 1e-09  YINC: 0.02  FRAMES: 18335  XORIGIN: -1.7e-07  YORIGIN: -77.0  TRIG: 0.0716\n",
      "CAPTURING: 229.66\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        xorigin = np.array(hf.get('XORIGIN'))\n",
    "        trig = np.array(hf.get('TRIG'))\n",
    "        capturing = np.array(hf.get('CAPTURING'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' XORIGIN:', xorigin, ' YORIGIN:', yorigin, ' TRIG:', trig        \n",
    "        print 'CAPTURING:', capturing         \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 256):\n",
    "            #if (maximum < 254):\n",
    "                x.append(maximum)\n",
    "                #x.append(maximum-np_data[2])\n",
    "                #x.append(maximum-np_data[30])\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append(minimum)\n",
    "     \n",
    "for f in glob.iglob(\"./compare/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./compare/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(200,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2204956677864809"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ./data/data07_1559318603-CHAN1.h5    ./data/data03_1559318400-CHAN1.h5    ./data/data09_1559318704-CHAN1.h5    ./data/data02_1559318349-CHAN1.h5    ./data/data04_1559318451-CHAN1.h5    ./data/data08_1559318653-CHAN1.h5    ./data/data10_1559318754-CHAN1.h5    ./data/data01_1559318299-CHAN1.h5    ./data/data05_1559318502-CHAN1.h5    ./data/data06_1559318552-CHAN1.h5"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        xorigin = np.array(hf.get('XORIGIN'))\n",
    "        trig = np.array(hf.get('TRIG'))\n",
    "        capturing = np.array(hf.get('CAPTURING'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        #print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' XORIGIN:', xorigin, ' YORIGIN:', yorigin, ' TRIG:', trig        \n",
    "        #print 'CAPTURING:', capturing         \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 256):\n",
    "            #if (maximum < 254):\n",
    "                x.append(maximum)\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append((maximum))\n",
    "     \n",
    "for f in glob.iglob(\"./data/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./data/**/xdata*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,129), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simply replot the current x, y in a nicer way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.tight_layout()\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,203), range= [[25000, 60000], [48,250]], norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [steps of ADC multiple by sample time]')\n",
    "plt.ylabel('Amplitude of Pulse [steps of ADC]')\n",
    "#plt.xlabel('Plocha pulzu [krok pulzu krat vzorkovaci cas]')\n",
    "#plt.ylabel('Amplituda pulzu [kroky ADC]')\n",
    "plt.title('PCRD, V_bias 3 V, step of ADC = ' + str(yinc) + ' [V], step of time = ' + str(xinc) + ' [s], frames = '+ str(all_frames))\n",
    "cb = plt.colorbar()\n",
    "cb.set_label('Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flat histogram (only X or Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Surface')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ble = plt.hist(y, bins=250)\n",
    "plt.yscale('log')\n",
    "#plt.xscale('log')\n",
    "#plt.ylim(0,200)\n",
    "#plt.xlim(1000,140000)\n",
    "plt.xlabel('Integral')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Surface')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Amplitude')"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "ble = plt.hist(x, bins=256-40-1, range=(40,256), histtype='step' )\n",
    "plt.yscale('log')\n",
    "#plt.xscale('log')\n",
    "#plt.ylim(0,200)\n",
    "plt.xlim(40,256)\n",
    "plt.xlabel('Ch.')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Amplitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cut Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/Na/data04_1559143613-CHAN1.h5 XINC: 1e-09  YINC: 0.0015625  FRAMES: 10090  YORIGIN: -119.0\n",
      "./data/Co/data02_1559142413-CHAN1.h5 XINC: 1e-09  YINC: 0.0015625  FRAMES: 3757  YORIGIN: -119.0\n",
      "./data/Co/data01_1559141460-CHAN1.h5 XINC: 1e-09  YINC: 0.0015625  FRAMES: 4844  YORIGIN: -119.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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4jNA6jSRJ0q7UK7hyvEdd87n6ffVcGcY5tra4zmyotdnSaFuZqPXVDOrsNna9rFco5zCGdO5oYKokDbEROm3EnReSJEmSJGmoDXTxIiKOiIj/jIjvRsRPI+JLEfGsWv1eEXFKRHwhIm6KiB9ExEci4kGNflZHRHb4OXuQ30eSJEmSpF2iyrwYxM8QGPQ0jgeuBl4E3AAcDpwVEftl5nrg/sBzgX8GXgnsCbwc2BwRD8/Mbzf6ewnwmdr9DUs8f0mSJEmSNGCDXrx4SmbWFxgujIj9KRY11lMsbPxqZv6sahARlwLXAscApzT6+0Zmfn6J5yxJkrQTmlkL1f0Es3kUverWNdpUNtYyLrbNvW43yfY8jHljbGR+DsbGRptxZnMwmv3U+1sOORLLYY6StAieNrJ0GgsXlcuBe5X1N9cXLsqyG4FrqjaSJEmSJGm0DENg52OAK7pVRsQ9KZb0O7X5l4i4PSK+HxFvjYi7LtUkJUmSJEnSrrFLN5lExKHA0yheCenmLcBPgXoY563APwKfAH4MHAKcAPxq2Z8kSZIkSSvbCB2VussWLyJiNXAWcG5mntGlzV8Ca4FnZOb2lzgz8/vA39SaXhwR1wHviojfyMyvdOnvOOC44m7vnf4OkiRJndVzIJpZC9MdPjdzMSZqbdY32tTMbOgyftXPeubmV3TTa449xu/4rCRJ/bdLXhuJiH0pUp+upVic6NTmqRT/tjwhMz/SotsPlteDujXIzNMy8+DMPLg4yESSJEmSpGVoxI5KHfjiRUTsCZwH7A48OTNv7tDmMRSviZyamW9u2XU2rpIkSZIkaQUY6BpKRIwB51DshXxsZl7foc1DKRY3Pg48fxHdP7O8fmln5ylJkiRJ0lAbsaNSB/1V3wUcDrwA2DciHl2ru5wiiOLjFAGd/wD8ZkRU9T/OzCsAImIK+CXgMxSBnb8NvBT4cGZ+dem/hiRJUif17IoWGRFjU3OrZjb36LNSz8NoZmZ0eqZqs668buw8l47Pd8rnkCRp8Aa9ePHE8vqODnUHAKuB+5b3FzXqL6E4VQTgSuAlwLHAXSmyM94MvK5/U5UkSZIkaUi582LpZObqBZpspfhHsFA/ZzP36FRJkiRJkrRCjdA6jSRJkiRJK8huu3oCg7NLjkqVJEmSJElqy50XkiRJO2yyvFYhmL0CLtc17qdhZkOX/tYzG5pZBXRuaYwF84M6mwGe9eDQ9czXKZiz070kaeiMWOaFOy8kSZIkSdJQG6F1GkmSJEmSVgh3XkiSJEmSJA2PEVqnkSRJgiLnYTGZDs3siXrmxMZGm8pkox0wvqq4Tm8u79fAdJVjMd24AmNri+vMVGOMTjkV1ecqV6NTvkUnC/0eOnwPSdJwCDxtRJIkSZIkqY2IOCIi/jMivhsRP42IL0XEsxptLo6I7PCzR5sx3HkhSZIkSdJyM1yZF8cDVwMvAm4ADgfOioj9MrO+HfAi4BWNZ29tM8DwfFVJkiRJkrQcPSUzb6jdXxgR+1MsatQXL27MzM/vyAAuXkiSpBGzmLyLevvq2iYzo0P99FT5YbJxXyur50vMbCg/rJtf13Ue62t11TwW+33rzLuQJC2ssXBRuRx4Wr/GcPFCkiRJkqTlaLj/on8McEWj7IkRcUv5+VLgpZn51TadGdgpSZIkSZL6JiIOpdh18Y+14kuAFwC/DxwH3B+4NCJWt+lzuNdpJEmSJEnSfIM9KnW/iLisdn9aZp7WqWG5GHEWcG5mnlGVZ+bJtWaXRsQm4ErgheVPTy5eSJIkzcmIgCKDYkujTVk3traWRzExt8nYmuK6Gpje0rvNDMxmSow3rpPMz5uYbrTpNf+dybmQJGmeGzLz4IUaRcS+FP8CuxZY26ttZv4gIj4DHNRmAi5eSJIkSZK03AzXUalExJ7AecDuwJMz8+aWj2abRmZeSJIkSZKkHRYRY8A5FNsNJzPz+hbP/DLwWOBLbcYYonUaSZIkSZLUynDtvHgXcDhFIOe+EfHoWt3lwK8Bb6BY4LiGIqzz5cAdwNvbDDA8X1WSJEmSJC1HTyyv7+hQdwCwjWK55Q3AKuAnwMXA0zPz2jYDuHghSZJWgGZg5c4+t7F73cw2OKzMINu0uVE3VXuker4K7pye24bxWpuNc9uwvjGXuk7f0YBOSRpJQ/IXfWaubtHs8J0Zw8wLSZIkSZI01IZknUaSJEmSJLUWwG67ehKD03XxIiI+sIN9viwzt+7gs5IkSZIkSXP02nnxTIpU0B+37CuAxwNvBLbu3LQkSZIWo8p8GO9QVllXKx+np7FVs59nylyLY9cU19M3wKbmeJON6zgcVvaxaUOjbX1eE+V1Y482zbHq92ZdSNLIGq7TRpbcQl/1LzPzC206Ks91vW3npyRJkiRJkjSr1+LFKcB3FtHX7eUz39upGUmSJEmSpN7ceVHIzFMW01FmJsXihSRJkiRJUt8suE4TEXsANwFHZuZ/LP2UJEmSdlQ9z6KZa7G+Q/mW8joxt25mav7zp1NrW7Wvnl/PPJuqD73yNTY27usZFs2Mi2a+hXkXkqTRseDiRWb+PCKuB2YGMB9JkiRJktTGCB2VeqeW7d4NPD8i7ryUk5EkSZIkSWpqG++xD/AwYGtEfBK4DshafWbmCf2enCRJkiRJ6sDAzo6eAdxafn58h/oEXLyQJEl90Mx6aJbD/MyJ5n39+XVzuxlbNfv5kDXF9eLyfmZbOdQUbG2Ube9vvPZ5srw2My/WMZtnUX+ufu2Vb9H8LEnSaGu1eJGZByz1RCRJkiRJUksjtvOibeaFJEmSJEnSLtF68SIiHh4R74+IqyLi1og4qCx/XURMLvS8JEmSJEnqk2rnxSB+hkCrxYtyceJLwL2BM4H6qSO3Mu9lUkmSJEmSpP5ou4byBuCMzHxuRIwBJ9fqvgL8Rd9nJkmSRlQzqLLa4FkP4xxvlNXDPBtlVUDn6qr7bbNjbKo/B9uDN6cnmQ3cbM5jPfODOrv001GnIE7DOSVJO2C3XT2BwWn72siBwPvLz9mo+zGwb99mJEmSJEmSVNN258X1wAO71D0UuLY/05EkSZIkSQvytJGOzgZeHRGPq5VlRDwIOAH4977PTJIkSZIkifbrNK8CHgJcAvygLDuXIsDzE8Dr+z81SZK08nXKsyiNrS2uMxvmP3NYlWNRtjmjrJoBxteUdWXZps3FdbrMpXjjKjix23zKDPKxVfDasp8Tt5V1VQZG/ZC1yUZdL+ZaSJL6aMR2XrT6qpl5K/AHEXEocCiwH3Aj8MnMvGAJ5ydJkiRJkkZcq8WLiLg/8P3M/CTwyUbdGLB/Zpp7IUmSJEnSoHjayDxXA4/oUvfrZb0kSZIkSVLftX1DJnrU7QHc2oe5SJKkFaHMlmiV8VBlXVRtx4GJ4uPMVFk2yVzrYVNZNrZmfpfTZUbGdOO5sTIn48QNs2NsH79qu74cmx65GPXvNd6tUQeL+b1IkqS6rosXEfFw4DdqRYdHxIGNZnsAfwx8cwnmJkmSJEmSOjGwc7s/BE4uPyfwt13aXQ08r5+TkiRJkiRJqvRavHg98PcU6zk/Bp4AXNZoc1tm/mKJ5iZJkiRJkjpx50WhXJT4RUTsAbwXuDUzbx7YzCRJ0hBq5jZ0ynFo1jXLgbGp4jqzudFmYva58bLNa8uqo7bNLQfYWl5nNsw+P7a2LCvbs7G4HFLmY2yq8i7qc1rfmMe62ueqrlO+xWLyK8y6kCRpRy142khm/hw4kiLfQpIkSZIk7WpBcVTqIH6GQNujUi+keG1EkiRJkiRpoNq+IfOPwOkRcTfgY8B1FCGe22XmFX2emyRJkiRJ6sTMi44+Xl6PL3/qCxdR3g/JZhJJkiRJkrSStF288JURSZLE/NDJ+n2nQMt6m1r99oDNyblNx9bM1k2XwZpHbZzbdro+5pbyWoVwjtf6boy7qQoHrfrrpRngWe9PkqQh4c6LuTLzkqWeiCRJkiRJUieLWqeJiEngYOB+wGsz89qI+G1gOjO/txQTlCRJkiRJDWZezBcRvwz8J/BIihPVDwBOBa4F/hz4OfCXSzNFSZIkSZI0ytqu06wH7g4cSLF4cVutbhNwcptOIuII4NkUiyB7A98A/j4z39do91zgZRQ7PL4OvCwzP9locx/gncDvUSyenF22u6Xld5IkSYtW5VY08x/Gmc2daLQdW1VcZzbDYWuKz1sbTafLHIoZmJ+DMTX7PFDkXEzWPlNkZUAt76I+V+a2ZbL2uZnHYa6FJGmZCEbq2Iw7tWz3JOCkzJymcUQq8B3gPi37OR74KfAi4KnARcBZEbGuahARR1Hs6jiT4r8uvg6cFxEPq7UZA84HHgAcCbwAOAI4reU8JEmSJEnSMrGYN2Ru71K+H/Czln08JTNvqN1fGBH7UyxqVLHepwD/mpmvAYiIS4BHACcCa8s2RwAPBsYz8+qy3S+AsyPilMzcgiRJkiRJK9WIZV603XlxKbAuIuqbUqodGMcAF7bppLFwUbkcuBdARDwQeBDwgdozdwDnMHcP6STwxWrhovQfFK+zPKnNXCRJkiRJ0vLQdp3mBODTwNeAj1AsXDy3fJXjYcCjd2IOjwGuKD8fWF6vbLT5H2DfiLhnZv5v2e6KeoPMvC0irqr1IUmSWhune/5D/X6iS11dI4didVW+BjZVmRRVP2XWRfX/KMbWzOZWjJUbLrdnXVSma8/RpU0n041rpzpJkjSMWu28yMyvURyRehlwNMUrJH8EfBtYk5nf3JHBI+JQ4GnAP5ZF9yivP2o0/WGj/h4d2lTt7tGhXJIkSZKklWVsQD9DoPU0yrDOZ/dr4IhYDZwFnJuZZzSHazbvUN5sU7XrVF6NeRxwXHG3d9upSpIkSZKkXajVzouIOCUiHtyvQSNiX4r9ntcyG8IJszss9mk8Ut3/qNau2aZq12lHBgCZeVpmHpyZB8Oei563JEmSJElDoToqdRA/Q6DtzovnASdFxBXA+4D3Z+ZVOzJgROwJnAfsDjw5M2+uVVdZFwcC19TKDwRuLPMuqnZzsi0iYnfggRTHrEqSpJ6auRZ1ZdnYVHFdXRVvqLWpni+zK964Bk7c1qhrWA1MTzYKq9PSyzFnNjBflaFR/f+ONTBTjbW+0Xaa2YzvRi5Gz+8sSZKGWdvTRvYHfg/4LPBC4JsRcVlEvDgi7t92sIgYozg5ZAKYzMzr6/WZ+S3gmxRHoVbP3Km8r/8XyEbgURHxgFrZU4G7AB9vOx9JkiRJkpal6qjUEcm8aBvYeUdmXpiZzwN+BTgc+CrwSuDqiPh0y/HeVT77GorTQx5d+7lL2WYK+POIOCkingC8l2Kx4421fj5IsfviwxFxeEQ8C3gncFZmbmk5F0mSJEmStAwseg0lM28Hzo+Ii4ELgL8Hfqvl408sr+/oUHcAsDUz3xcRd6c4nvVVwNeBPyhPPKnm8IuIeBLFgsUHgFuBs4GXLvb7SJIkSZK07FQ7L0bEor5qRNwZeBJwJPAU4K7AJcDftnk+M1e3bPce4D0LtPkO8PQ2/UmSJEmSpOUrMrueLDrbqNjlcCTFYsFewKeB9wPn1EI0l5WI/XP7qamSJK1o43QPqayHW3YLtOxU3gze3NilDGZDOettqrqqz24hm930O3yzGTJqqKckLX+nfKk4aXJlOvjAyMv+eTBjxePY5b/LtjsvPgZ8ATgF+EBmfm/ppiRJkiRJkjSr7eLFAzNz61JORJIkSZIktTRimRdtTxvZusTzkCRJkiRJ6qjVOk0Z1PkC4I+A+wJ7NNtk5r36OzVJktRep1yLTrkQ6+hsEg5bU3zctKF8fG1x3VprtrrqcnPj+XUwvqqs2zB3/LGyfGYCWD87Xt1YOfbMFooT0utlVX/V9+iV4bGjzLiQJC0zI7bzou1XfRvwPOA84CLgtiWbkSRJkiRJUk3bxYsjgBMz8y1LORlJkiRJktTCiO28aJV5QfFr+epSTkSSJEmSJKmTtus07wGeBVywhHORJEk7rFNmQ6eMiPI6L09iEjZtnv0MMD01937OMOPMtbHDFMrnZqaaFfPNybUoO5rZ2GUs8ykkSRo1bRcvrgP+b0RcRLGA8aNGfWbmP/V1ZpIkSZIkqbvddvUEBqft4sXby+v9gd/pUJ+AixcRjfKNAAAgAElEQVSSJEmSJKnvWi1eZGbbbAxJkiRJkrTURiywc4S+qiRJK0Ez/2GivG5hNgtislZWtSnLDltVXC8uq8bWFtdDgGPLuqO2Fddjp+a2nd7cYfz6mM0siuZ8Ntae39Jo04sZF5IkjbrWixcRsQ/wPOBxwL7AjcClwGmZ2czAkCRJkiRJS2XEdl60eh0kIn4V+H/Aq4G7AdeW11cDXy3rJUmSJEmS+q7tOs3bKE4YeXRmfrcqjIj7UOwBfSvwtP5PT5IkSZIkdTRCp420DeI8BPjb+sIFQHl/CvCEPs9LkiRJkiQJaL/zIum+pnOnsl6SJC25buGV08C6RtmW2rX8vKmqq0I0y/42bZlfd0Z5O7N5fj/z5jO+QFmzTpIk7RQzLzq6CHhNRDygXljevxr4ZL8nJkmSJEmSBO3XaV4IXAhsiYgvA9cB9wIeCXwbOH5ppidJkiRJkuZx58V8mbkVOBB4PvB14M7AFcDfAA8u6yVJkiRJkvqu9TpNZt4GnFr+SJKkJVNlRUw07qE45KtT3WyWxJ1+sAcAdzxubVGwusMQF5fXmaqbNbN10xvK58rnp6sx19E9z2J9raxZZ86FJEl9586L+SLi0Ig4ukvd0RHhaSOSJEmSJGlJtF2neR3wkS51+wHPA36rLzOSJEmSJEkLym5ngq5AbU8beShwWZe6y4GH9Gc6kiRJkiRJc7XdeTED7NulblWf5iJJ0ohq5lZU14nGfa1srMyomNk8ez8zBcAd9270Nz1Ze37L3KHHqlyLDbVxJmtldet7zL05z05tzL6QJGkliogjgGdTnEi6N/AN4O8z832Nds8FXgbcj+IwkJdl5ifbjNF258WngZdGxO6NgXcHXgxc2rIfSZIkSZK0kzLg9rHB/LRwPPBT4EXAU4GLgLMiYl3VICKOojgA5EyK/1PydeC8iHhYmwHa7rx4JcUCxnREvB/4PvArwB9TrKo8p2U/kiRJkiRpZXlKZt5Qu78wIvanWNSotm6eAvxrZr4GICIuAR4BnAisXWiAVosXmfnViHgUMEWxFWQVsA34JHBKZn6z1deRJEmSJEk7L1rvilhyjYWLyuXA0wAi4oHAg4AX1J65IyLOqZf10vqrZuY3gGe1bS9J0uhqZjyMd2gz0aOszKWoci2OBs4oq2a2zZYBbC3bbNoMY1NlmyoHo9P/xKiyMsp+ypyM7TkX9XFnaNTVMysa2Rk9mXUhSdIIegxwRfn5wPJ6ZaPN/wD7RsQ9M/N/e3U2JOs0kiRJkiSprQyY2a1tjOXOumNRrSPiUIpdF8eURfcorz9qNP1hrd7FC0mSJEmStMP2i4jLavenZeZpnRpGxGrgLODczDyjUZ3N5l3K53HxQpIkSZKkZSYjuH1sUH/S33ZDZh68UKuI2BfYCFzL3BDOaofFPsBNtfJ9ymtzR8Y8g9pjIkmSJEmSVqiI2BM4D9gdeHJm3lyrrrIuDmw8diBw40J5F+DOC0mSlkCngMqybGyquM5sKO/XzoZnNp87pLyePlUrLMM/T6/arputqvqs+pmpQjUnmA3YrAVz1o2tmW1fBX6ysXNbSZI0FG7fbbddPQUAImIMOIfiPzoem5nX1+sz81sR8U3gCOD88pk7lfet/oOj1eJFRDwIeDTwy2XRdcDnMnMxUeOSJEmSJGnleRdwOMWxp/tGxKNrdZdn5q3AFLAhIrYCnwH+jGKx40/aDNBz8SIi7g/8C8X/+4lGdUbEhcAxmfntNoNJkiRJkqSdlwS3Mxw7L4Anltd3dKg7ANiame+LiLsDJwCvAr4O/EFmfq3NAF0XLyJiL+AiYA/gr4ALgO9QLGLcB/h94CTgwog4KDN/0uorSZIkSZKkFSMzV7ds9x7gPTsyRq+dF38J7AX8emZ+r1H3LeCfIuKjwJeBvwDevCMTkCRpeRlv3E/XyiaKy9ia4ro9g2KS2RyKMt9irAzgntlca8/c57eW98dOzQ53eiMf4+xVZfkq2FTNo3x1dHztbD9V36zv/D1mpmpl1aunk417SZI0LJJgZnh2Xiy5XqeN/AHwrg4LF9tl5neAU4Gn9ntikiRJkiRJ0HvnxYOB17fo47MUr5VIkiRJkqQBuX2EDhDttfNib+DGFn38qGwrSZIkSZLUd72WaXYD7mjRR9J7EUSSpBVkukddmQ0x08jAYCOz+RFV9kV5HVtTy8FY1RiqzMyYnmBeHkWVmXHUVFk+CWypfQamt819Bmr9dPoezbLquV7PSJIkLb2F9picERE3L9Dmbv2ajCRJkiRJWtiQHZW65HotXpxJsauijS/0YS6SJEmSJEnzdF28yMyjBzgPSZIkSZLUkjsvShHxOeA84L8y8yuDm5IkSbtalU+xhdmch8m5TcbWFNeZbbNtqhyK1WWb1WWGxcVrZsu2rmq0ATY1cjC2m5g7FsDMRG1cYGyqdl9lVKxv9DPO/LyKxeRYmHUhSZJ2rV6vjXwEOBw4OSL+F/gY8F/ABZm5UA6GJEmSJElaQqO086LrKSGZ+XeZeQhwT+B4YHfg3cC2iPhERLwgIsa7PS9JkiRJktQPC502QmbeBLwfeH9EBLCGYkfGnwJvi4gtFDsyzsvMC5dyspIkSZIkqci8mHHnRWdZ+Hxm/m1mPhK4L/B3FG/sfmQJ5idJkiRJkkbcgjsvesnM70XElcBBwH79mZIkSbtaFXw5DqwrP0/PrZvZUt5PwHgZqLmVudfpKlRzFUxP1foEpsvgzemqn/oYjbcyZzbWbiZn+wSY2dyYcycTzA/dNIRTkqTlrDhtZKf+pF9WFrXzohIRvxERb4qIa4BLgSMz8xf9nZokSZIkSdIidl5ExIOAo4BnAQ8qiy8AXkiReSFJkiRJkgZklE4b6bl4ERH3BY6kWLB4BHAHcDFwKvA24PWZ+aklnqMkSZIkSRphXRcvIuIS4LFAAJ8HXgB8IDOvj4i9KRYvJEkaclV+RJXxMNmo3wjjU2WTMqOC9eV1YjZb4ujySplvcXF5uxU4pPx8RnldXQ1ZjjkzzfbsjHlZFcDY2rJsG3NVORbranOqMjequub36aRXHoYkSVqOiswLd14APL68fhJ4E3BhZt6x9FOSJEmSJEma1Wvx4ncpXhd5BnA+8L8RcQ5wNvD1AcxNkiRJkiR1kMDMCO286HraSGZenJnPA+4NPI1iB8afAZ8CrqD4Xd1vsQNGxHhEvDsi/jsibo+Iixv1h0REdvk5v9bu6C5t/mKxc5IkSZIkScNrwdNGMnMGOA84LyLuCjyVYkfG7wNnRsQrgDMz800tx3wocDhFjsbuHeq/DPxWo+z+wPvp/NLu7wI/q91/q+U8JEkrTpVvMVFeNzKbddFss3H2fnqq+DhWXmfKfArWw0z1XCProsrHGF8Fp2+YO8R0Nf6W8jrJ9syKmQ4ZFTNTXeZYWU935llIkqSVr/VRqQCZ+TOKRYT3l6Gdz6BYyHgNRS5GGx/NzHMBIuKDwH6NMX5MsbCxXUQ8nuKkkw906O+LmfnTxXwPSZIkSZKWt+D2xf1Jv6x1fW1kIZl5U2a+NzN/D7jvIp7bkdDPZwGXZOb3duBZSZIkSZK0jPVlmSYzr+9HP51ExATwCOC4Lk2uiohVwFXAWzPz3Us1F0mSJEmShoFHpQ6fZwG/AD7UKP8+8CrgC8BuZbtTI2LPzHzbYKcoSRpO62BsVfHxkLJo0+byQ5lLMbYGji6LtlZtyuuxU3B6mW1xRrPvMmtie74FzOZrTM5tw/T8srGp4jpTz6yo2mxBkiRJs5bD4sVRwCcy88Z6YWaeT3GEa2VjRNwFOCki3tHp9ZSIOI7tOzj2XrIJS5IkSZK01EZp50XXzIuIuH9E3HmQk+kwh18HHgy8r+UjHwT2BVZ3qszM0zLz4Mw8GPbszyQlSZIkSdKS6hXYeTVF1gQRcWFEHDiYKc1xFMUxqOcu8rlcgrlIkiRJkjQUqsyLQfwMg16LFz9jdnvCIcBeSz6b+Y6kOFq17VGozwBuAK5ZuilJkiRJkqRB6pV5cTnwjoi4oLxfFxHf79I2M/OENgNGxJ7A4eXtfYC9IuKZ5f3HMvOWst2jgQOA47v08yGKsM6vUgR2Hln+PH8Hj2OVJC07413Kt8xeV68tPl7ceKYK8pzZAGeUbWbKMM/xNcX19M2zfc1UYZqdQjmrMmpl9TbjtbLSzFSH79FoI0mS1EUSzAzJrohB6LV48VzgzcDTKF7DOBS4tUvbBFotXgD3As5plFX3BzCb9X4UcBPd/0vuG8AxwP2AAK4A/jQz/63lPCRJkiRJ0jLQdfEiM68EngIQEXcAT8/ML+zsgJm5lWKxYaF2LwRe2KP+FcArdnY+kiRJkiQtR7cviwNE+6PtNz0A6PbKiCRJkiRJ0pJptXiRmddExFhEHAk8juI40huBS4EPZ+bMEs5RkrQijTM/K6KXKj9iS61sotYXtRyLbcX1sFWzh2dfXF63lm2q8q1r5w81PVUbc7JRWY1ff6uxbDO2Zu74rK+1GW9c6999Mb8HSZKk2dNGRkWrxYuIuBfwCeDhFJkU1wG/Bfw18N8R8cTM/N+lmqQkSZIkSRpdbV8beSuwCliTmV+sCiPiUcCHyvpn9396kiRJkiSpadR2XtypZbvDgRPqCxcA5f3LgSf3e2KSJEmSJEnQfufFXYCfdKn7CbB7f6YjSRodvXIexmufq8yJKmNiula+cW6bmcbp2ptgNhdjy9y20+vn3s8Zd7J235xnp3mX485sabSp51uYayFJkrSj2i5efB44ISIuzMybq8KIuBtwQlkvSZIkSZIGZGaEXhtpu3jxYuAi4NsR8QmKwM57Ab8PBHDIksxOkiRJkiSNvLZHpX4lIiaAlwCPojh15PvAqcBbM/OGpZuiJEmSJEmqKwI72+5HWP5af9NygeLEJZyLJGkk1HMgJnvUlQ5bVVxXry2uW8vy1wKPW1N8PqQsW71mbputwPS2zn2PTZXP1NrPVG2r7Ip69sYWOqvnYrTJx5AkSdJijc4yjSRJkiRJK4RHpUqSJEmSJA0Rd15IkiRJkrQMufNCkiRJkiRpSLjzQpK0g+phlosJpqza1p4fL4M2p6cadROwqQrRrJ4rgzM3Tc6WbSrLDitDPTfV+2kEdM6sL6/1bjeWN80A0fV0DBGdw1BOSZI0eEkwM0I7Lxa1eBERTwR+E/gViqNSN2fmBUsxMUmSJEmSJGi5eBER+wMfAR4FXF/+3At4dURcBvxhZn53yWYpSZIkSZK2K04bGZ2XKdpmXpxGsdvicZl578x8eGbeG3g8cG/g3Us1QUmSJEmSNNraLtP8LnBMZn62XpiZn4mIE4H39H1mkqQhV896qGVUALMZEnWTc9setgourroqcy3GporrIWX5anjAe64E4JqJKhejNv5YWVblV2zPx6jncawr22wuq8oxpjd3mOuW2b7nWSj7QpIkabA8bWS+64Cfdan7GXBDf6YjSZIkSZI0V9udF6+nyLf4UmZ+pyqMiPsCJwOvW4rJSZIkSZKk+YrMi9HZedF28eKJwCrgqoj4MrOBnQeVnw+LiMPKtpmZR/Z9ppIkSZIkaSS1XbzYj+JF4Opl4L2AnwNVBsY9+zwvSdIu1ybjodmm3rbMmmB9eS3/FVLlVGyaYjYHo8ydmCmf2VQ+MzbFNaffVH5ujrlxNutiu2bWxkStrJzb9JZGm/Eu86dHmSRJkgap1eJFZj5hqSciSZIkSZLaGbXXRtoGdm4Xhf0jYnQOlJUkSZIkSbtM68WLiDg8IjZTvC7ybeDhZfl7ImLtEs1PkiRJkiR1MMNuA/kZBq12T0TEnwLvBf4deBfwL7XqbwLPATb0fXaSpF2oynpo5FJsNw6HlWvXF5dFM5vLqjWzzQ6Z2t58Trenr4PxVeVNrT0AU7VplA/ONP81M9Gh0+o6XmvX/B7NzItOmRZt8j4kSZI0KG1f/Xgl8ObMfHlE7MbcxYuvAy/p+8wkSZIkSVJHRebF6KQ5tH1t5AHABV3qfk5x+ogkSZIkSVLftV2m+TbwCODCDnUH475aSZIkSZIGxtNGOvtn4OQymPOuZVlExKHAy4D3LMXkJEmSJEmS2u68eBNwP+BfgdvLss8CuwHvzsx/WIK5SZKWzGICKaugznXldf3ss5saIZpjZYDn9GYYK0M4T2+EeE6X92ypDT9RPl+2mRfO2W2uzRDRXvULtV1oLEmSpOEySjsvWi1eZGYCfx0RbwUOBfYDbgQuzMxvLuH8JEmSJEnSiGt7VOpvA1/OzKuAqxp1dwMemZmfWoL5SZIkSZKkhiSYGaGdF20zLy4CHtKl7sCyXpIkSZIkqe/aZl5Ej7q7A7f0YS6SpL4Zp3duw3StXV2ZPTG+Bqa3lWVlVsTYquL62qnZLo4tm2wtryeV19Vr4OLy8xvLHIsTy6yLw9bMtmnmYVT9VPOYY7Ix9410z+6of79u39VcC0mStHwVp420/ZN++ev6TctXRQ6pFR0bEU9qNNsDeDLw//o/NUmSJEmSpN47L9YwGy2fwBHATKPNbcCVwEv7PzVJkiRJktSNp40Amflm4M0AEXE18IeZ+ZVBTUySJEmSJAnaH5V6wFJPRJLUD90yIOp1wGFri+umMtfi2DLPYmut+day7JCqbZlPsWnNbJsq4+K11bAbyuvEbI7FiduYo+qHLbNl023mXWZvzMnDaLZv5lrUmXEhSZJWjiLzYnR2XrQ6bSQinhERz6ndHxARn42IH0XEhyJin6WboiRJkiRJGmVtj0o9Cdirdr8e2A94I3AQ8Lo+z0uSJEmSJAlof1TqAylPFImIvYEnUmRg/FdEXEuxiPHXSzNFSZIkSZLUNCyvjUTEOMVBHo8GHgZcmpmHNNpsBR7QePS6zLx3mzEWcyhsltffAW4HNpX33wHuuYh+JElLbpLZjIgqB2Ji9n7ThrnNz1g7935mG8UmO2BT4/mt5e30ttmsjEc3ci1gNv9irOx7ZuPce9bATGMe2+dcGe9Q1iu7wlwLSZKkXeChwOHA54Hde7Q7i+3/kQkUJ5i20nbx4r+B/xsRnweOBS7KzFvLuvsD17cdUJIkSZIk7ZwkmBmSnRfARzPzXICI+CBFzEQn38/Mz+/IAG0XL14BfBT4M+CnFK+NVJ4ObO70kCRJkiRJWtky846lHqPtUamfjoj7Aw8CrsrMH9Wq34v7dCVJkiRJGpjiqNTFJEEMhWMi4vnAz4ALgBdn5jVtHmz9TTPzJ8CXOpR/rG0fkiRJkiRpJJ1LkYnxHeDBwMnApRHxfzLzpoUe7rp4Ua6GnJ2ZrfMsymfOyswb2j4jSVpIFZg53aGsCuHc0uGZss1hjTDO1wJry7LVjcc21YI3x6bmtjmkvJ5etZmGi1c15lbNh1pQ5+a5dTMdwj27buBzY58kSVI3AzxtZL+IuKx2f1pmnraYDjLzBbXbSyPis8BXgD8H3r7Q8712XrwN+BwtwzgjYrfymU8DLl5IkiRJkrQy3JCZB/ezw8z8WkR8AzioTfteixcBvCEibmw5drRsJ0mSJEmSdkKReTE0p43sjGzTqNfixaeA3YB7LmLQTwE/WUR7SZIkSZI0YiLiYcCvAe9u077r4kVmHtKnOUmS+qKefdHMgphutAGYLC6bNs+t27oKtlaPbSirqlyMjbXny7Kq7elT8+cxXW9fNwkzU13qKvW5NvvplPMhSZKkShLMDMnOi4jYEzi8vL0PsFdEPLO8/xjwBIr/uDwP+B5wIHAScC1wRpsxlt25KpIkSZIkaajcCzinUVbdHwB8u2zzdmAfYBvwceAVmfnjNgO4eCFJkiRJ0jJ0+5D8SZ+ZW1k4B/PQnRnjTjvzsCRJkiRJ0lIbjmUaSRo5O5LpMFFcDls7W7S6vB5WXO50yM3bq9b88mbqPrd5FQAPWHMl10wcWJaundvP9MTs/I4uP56+rbgeO1XeT8222a7M19ieXbGxQ1lT/bs3fx9mXUiSJPWygk4baWXgOy8iYjwi3h0R/x0Rt0fExR3abI2IbPz8oEO7h0TEJyPiloj4XkS8OiJG55+eJEmSJEkjYFE7LyLiIcAjgfsB783MH0TEOHBdZrY9IvWhFCmknwd279HuLGB97f62xlzuAWwCrgCeBvwq8BaKBZmTWs5FkiRJkiQNuVaLFxFxd+C9wDOAmfK5jwM/AF5PcbzJS1qO+dHMPLfs94PAfl3afT8zP9+jn78A7gr8UZlOekFE7AVMRcTftU0slSRJkiRpuRm110ba7rx4K/AYireqPwP8vFb3MYqFi1aLF5l5x2Im2MMkcH5jkeJs4E3A7wAf7dM4ktRn4/TOdFhXXpttylyITdtmi8aKHIsql+KO8fJ+ehufO+x3G+03AHDN+FqYnpuHwXSVOVHlU6ybzbqo5nF6p+yKZkZFfc7dsi46MeNCkiRJ3bXNvPgj4ITMvAi4vVF3DfCAvs6qcExE3BYRN0XEByOiOcaBwJX1gsy8FrilrJMkSZIkacW6nd0G8jMM2u68uCuwrUvdLzF/QWNnnUuRifEd4MHAycClEfF/MvOmss09gB91ePaHZZ0kSZIkSVoB2i5efBH4U4qci6ZnAp/t24yAzHxB7fbSiPgs8BXgz4G315t2eDy6lBMRxwHHFXd792OqkiRJkiQNXBLMDMmuiEFou3hxErApIjYB51AsDhweES+iWLz47SWaHwCZ+bWI+AZwUK34h8A+HZrvTecdGWTmacBpABH7d1zgkCRJkiRJw6XV4kVmfjoiDgXeCLyTYnfDKRSvdhyWmV9cuinOnUrt85U0si0i4n7A3WhkYUjSYI037qcb5RMwtrb746ur66q55VvL62uBo8o3+Y4uy84o224o7x9Xe/aw6nM55sXA2WvmFDFTPTg5+1wVBjrTDN6sfY/t320x4ZySJEnaWcVpI233Iyx/rb9pZn4GeHxE3JUybyIzb1mymdVExMP4/+zdfZweVX3//9cnRECCIAYFo0jUTcW7akvagNKvEaI11vs7oo2V8g3UUrFfrXKjUReMFbBqLXgHqcY2VlAQv8USKRFjRSAaWvXnDTar340gSCVGkQCazZ7fH+fM7uzk2t0r4drda7Ov5+Mxj7nmnDMzZ66d5JE9OfMeeALw8VrxOuCtEfGQlNKvS9mJwH3AVyejX5IkSZIkaeLt9jBNSuk+4L6IOCQifgf4QUrpN+3uHxEHAM8vm48CDoqIV5Ttq4Fnk/8v8IvA7eTZFSuBnwBraof6GPBG4PMRcT7wOKAX+EDj9amSJEmSJO11uuVNIJOhrcGLiDgH2C+ldFbZPp78RpADgJ9FxHNTSt9r85yPIOdm1FXbjwVuLW3+npxpsZUcFPq2+qBESmlbeZTlIuAqcs7FB8kDGJIkSZIkaS/R7syLPyU/ZV15P3A9OffiPcB7gRe1c6CUUj85M2MsJ7R5rO8Dx7fTVpI6o8p76BujvFnX4hjNjIkql2IFsGxjOVQ5Zk+p6+vN65W9w+dY3ciaOKaWWbGhyqyo3nRd9WszLFvQ6FO137rhNgPNrI7mdY13nZIkSZooOfNi5sy8mNVmu3nAj2EoFPNpwLtSSjcBHwCOmZjuSZIkSZKkma7dmRe/Jr+CFPJMh20ppW+U7fvJj49IkiRJkqRJkAgGZtDMi3YHL74KnBURg8BbyHkXld8h51RIkiRJkiR1XLuDF28C/hm4FPgW8PZa3Z8B/9HhfknSFOlh7CyH0XIg6vuUuiXL87q/cYi1QH+pW9mo6weWLMqfV5Sy+WW9vHd4u7+0GahOWbb7Ng73YXH5OJR9Ue9f8zoqra7dbAtJkqRutHP3XyA6bbV1pSmlnzJ6MOYfkx8dkSRJkiRJ6rgHPExTf32pJEmSJElSp406eBERn92dA6WUXvXAuyNJkiRJksYz016VOtbMi4dPWi8kqWuMle9Qz4pYWtbrynpBrU2xvrd8OD2vlpTsieNqhxzYmtf9pW5Di7K+rSPP1bd0+PPskp0xlHWxeXi9vjpJ1dcLR7km2PW6x8v+kCRJkibPqIMXKaVnT2ZHJEmSJElSe2bazItZU90BSZIkSZKksbQV2BkRF4zXJqV0xgPvjiRJkiRJasfADJp50e7bRl7ZouwQ4CDgV8A2wMELSZIkSZLUcW0NXqSUHtuqPCIWARcDr+9kpySpO/Q0tpfCTSVE85gSolkFZs4vTVYAi8vn1b0AzFq1HYDBNbl41trtDD56TmlfjtdfO00VtDkU1Fl1pxnOCQxUn0tfexaVNr2MHdRZGS2U07BOSZKkbpYzL9qdjzD9PaDMi5TSRuB9wEWd6Y4kSZIkSdJInRim2Qo8oQPHkSRJkiRJbZhpbxtpN7DzgBbF+wJPBM4FvtfJTkmSJEmSJFXanXlxD5BalAfwU+AlHeuRJE2YHnbNcmjmWvQxnBVRNVk0/Hl5VVayKqr8ifmlzWrgrFI2O5cNrr4yb6/IOw8efuXw8VbXz0s59+YWfQL6qr4uqPVxXVlvHnmYepkkSZL2Ss682NXJ7Dp4cT9wG/CNlNKOjvZKkiRJkiSpaPdtI2smuB+SJEmSJKlNMy3zYsy3jUTE8yPiqoj4/yLi2oj4y4iIyeqcJEmSJEnSqDMvIuKVwGXAfwPfAuaTX4n6WOCMyeicJHVeM+OikR3R05v/tgPYUNar8mrW4u0MHjcnbyzOqyM3HwzAlstK2/XA4pJ/0V+dowRlLKmOuxz61ubPs0vdQO9wl4bK1jb6WPW9mdtRL2tenyRJkvZGCRhw5gWQByg+AzwxpbQspXQMcDbwxojoxCtWJUmSJEmSxjXW4MUTgE+mlOpBnZeQX5H62AntlSRJkiRJUjHWDIoDgbsbZdX2QyamO5IkSZIkaXzBzrZfIDr9jXelz4iIQ2vbs8iP1jwzIg6vN0wpXd3pzknSA9PMf1hQ+7y5rEtWRJUz0dcL83vz5/mlybKtAAzSBz0j8yy2POeo/GH9xto5LyyfTy/rsh3CkvkAACAASURBVL2+vl3lWDRthoFmf9eN0raVvlE+S5IkSdPXeIMXHxil/EON7QQzKClEkiRJkqQpNNNelTrW4IW5FpIkSZIkacqNOniRUtoymR2RJEmSJEntm0kzL8Z624gkSZIkSdKUmznRpJL2cj3DoZsDW0tZCcqc3Ttc3jM3f141MnjzyDNvAWDLxl7YUHZfXNYryz7z5w5ngK5snH7JouHPVTBnda4qAHSofim7holWAZ7rMGhTkiRJ40kEA868kCRJkiRJ6g7OvJAkSZIkaZrJbxuZOb/SjzrzIiJ+HBFPK5/fGRHzJq9bkiRJkiRJ2VjDNI8CDiif3wV8Cbh9wnskSWNqZkUsKOt1w1kX55WsCXrzaiifog/6S92yjXl9U86q2BKbSptNjMyfKPsBcHqtrDpvsb6+Udr0leyLvr6R5SxlKI9jgBaqazT7QpIkSaObSW8bGWvw4kfAGyJiLhDAMyLi0NEap5Su7nTnJEmSJEmSxhq8OBv4FPBqIAEfGKNtghk05CNJkiRJ0hTKmRcz59fwUQcvUkr/NyIeBswDfgK8DPivyeqYJEmSJEkSjPO2kZTSIHBbRPw58LWU0tbJ6ZakmWm0rIfThz/OLpkV86um5a+lFb3Duy8u6yrr4qSyXrKII0+8BYAtz8lZF2yojrt8+LjVMXtqZVXbgZKHsaT0Y/3aUlnlZNT7e2GLOhjOvhiNWReSJEkaWyLYOejMixFSSp8CKG8cORZ4GPAL4MaUkiGekiRJkiRpwrQ1eBERs4CLgFMYmW2xMyIuBk4vszQkSZIkSZI6qq3BC+Bc4GTgbcBlwJ3AYcCJpW4r8M6J6KAkSZIkSWpIMDAwcx4biZTS+I0ifgL8Q0rp71rUvQV4Y0rpMRPQvwkTMS/BqVPdDUlDemqfmxkRLTIgbiqZFSWWgv7hqlm3bQdg8PDvNo69DlaUHTZUh946XDfauUb0rdTP7s3rgY2lfHOtfrTsDkmSJE2ec25OKS2c6l5MlNlHPy0deOM1k3KuX+33yCn/LtudefEI4Duj1H2n1EuSJEmSpEmQUrBzoN1f6ae/WW22+29g2Sh1y4AfdqY7kiRJkiRJI7U7TLMKuDQiHgNcTs68eATwSuDZjD6wIUmSJEmSOizPvJg5mRftvir1sxHxS+Ac4EPAg4AdwM3A81JK105cFyVJkiRJ0kzW9gMyKaV/B/69vDb1UOAuX48qac/1NLYXDH9cMrdRV9teUtb9Zb22rFcONxlcOaecooR6VqGcPctrQZ0bR7apkj/nA+ur9nNHtmXzcL93CeqsM6hTkiRJEyzhzIuxlAGL/5mAvkiSJEmSJO1i5kSTSpIkSZK0l0gpGNgxc2ZetPu2EUmSJEmSpCnhzAtJE6DKs+gbvWx2yZgYyo4AlpT8if6q6dpG27WwvmRjDGVVFCuA9eXzhmr/kl3BunLc5fkYwFDGRnWOSt/S4fZ9zeyKnto1mWshSZKkqRQM7pw5v9I780KSJEmSJHW13RqmiYgnAUcDRwCfSCn9LCJ6gDtTSr+eiA5KkiRJkqSGBPi2kZEi4kDgE8DLgYGy35eAnwF/C/wEeMsE9VGSJEmSJM1g7c68+ADwDGAJ8HXg/lrd1eSBCwcvpL1aT2O7VeZDs83Sst4MPSW3Yn4p6i/rVbWci6quyq6guc9yZl2/HYDBNSPbzjppO4Nnlb+aLp2b18uqbpXj9K2tfa5lbYzo6zqGr61eBkM5GfkASJIkSVMmhTMvWngZ8Ncppa9ERPPb2QIc2dluSZIkSZIkZe0Gdj4Y2DpK3UOAnZ3pjiRJkiRJ0kjtDl58E/izUepeAdzQ7gkjoiciPh4R346InRGxoVH/yIh4X6m/JyJujYhPRcS8RrvFEZFaLOe12xdJkiRJkqalBAzE5CxdoN3HRlYC6yNiPfA58tf0/Ih4E3nw4n/txjmfDDwfuAnYt0X90cBLgdXARuAwoBe4ISKeklK6p9H+T4Ef17Z/uht9kdS2KuOhyrVo5kHAcCZEKZtd8iUGeoYzLppZEytbZF4MnXLryFPPnsvgo+eUY1bHyecafHQv8N1ctKzep9r+9NXO32jDheyq2aa5LUmSJGkytDV4kVK6PiJOAM4DLgICOIc8ALEkpfTN3TjnVSml/wsQEZcDhzbqrweOSikNVAUR8Z/AD8lvO/lUo/13Ukrf3Y3zS5IkSZI0/Q2M32QyREQP8FbgGOApwNdSSosbbQI4G/hL8jjAN4E3ppS+1c452p15QUrp68AfRcSDgUOAX6aU7m13/9pxBsep/2WLsv+OiHuBR+zu+SRJkiRJ0oQa7wkLgLOAd5AHOW4B3kx+wuMpKaWfjXeCdjMvhqSU7ksp3b4nAxd7KiJ+FzgA+H6L6utKdkZ/RKxs8TYUSZIkSZL2Lok882IylvFdlVI6IqX0SuB7zcqI2J88ePHelNJFKaX1wCvLVbyhnROMOvMiIj7RVheLlNLJu9O+XRExC/gQsBn491rVr8iPsXwN+C3wAvKjLA8H/noi+iJJkiRJkkYa7wkL4BnAQcBna/tsj4iryGF6K8c7x1iPjTy1sf0Y8sDA/5TlEWX5ObBlvBM9AO8FjgWelVLaURWmlP4L+K9au/UR8RvgzRHx7pTSXc0DRcSpwKl56+AJ7LI03VQhnH2jlNctGFl3Xm9erwc2lKpVi0Yc7suXvJATFpSXEq1Y1LIHx555HTdednze6C+FK+fm9UAJ7pzPcIjn7Oo4i0a2AegpQaF9axt9huHQzVaBo5Cvq/k9SJIkSV2mmnkxPRwF7CRPSqj7AXBiOwcY9bGRlNIfVAtwLnAPcFxK6fCU0u+mlA4H/gj4NbBqT3o/nog4jfw8zOtSShvHaw9cTh6Q+d1WlSmli1NKC1NKC/NTKJIkSZIkaYIdAtyTUtrZKN8GHBARo+VkDGk3sPM8YGVK6YZ6YUrp6xHxTuB84F/bPFZbIuLl5HcXnpFSumw3d0+d7IskSZIkSV0lATvGbdUph0bEptr2xSmli3fzGK1+T48x6kZod/DiccBoAZ33kidzd0xELAY+DVyUUvq73dj15eSJM9/pZH8kSZIkSZrB7spPMOyxbcBDImKfxuyLhwL31iMiRtPu4MV/Ar0R8Y2U0h1VYUTMA3qBm9vtcUQcQH6FCsCjgIMi4hVl+2rgSOAL5FenXBYRx9R2/3lK6UflOB8l5218kxzY+XxySunfp5RqD79LGl+V8dDMuKjnWzRzIEpWxMqSLzG/VtWI2zmh/4bhrIqzyn6zR+5341lPA6o/uqNkb/RtHa4bqNo0MyuAvupRur7Gun59LfZree7pZrT8EkmSJO1VEjlFYnq4BdiH/I/VH9bKjyp142p38OJU8ps++iPiZoYDO48m/7axvM3jUPb7XKOs2n4sOX3vYOBpwNcb7T4FnFQ+/wBYAbyJ/B7ZPuBvgH/Yjb5IkiRJkqSJdQNwN/n1qKtgaGLDC4G2Hj9pa/AipfS9iHg8cDLwB8Dh5NGStcAnU0r3tdvjlFI/w8+1tLKmLOMd5x9woEKSJEmSNFN1ydtGxnvCIqV0b0ScB7wjIraRZ1u8mfwSkQvbOUe7My9IKd0PfKTd9pIkSZIkaUYY7wmLfvKLQGYBZwNzgU3Ac1JKd7ZzgrYGL8ooyphSSqMFekqaLnrKE2B91ZuJS35Cz9zhCIVL5+Z1/6K8XpxXP1g0nw9zGgAXnXLGyOOuAJaU/VY2njLrW5vXS5bDhlJWjSCvqM7F8LqvmeVwellfyPh5DzMhB2ImXKMkSZJIdM3MizaesCCllID3lGW3tTvz4h7Gf3XJPnvSAUmSJEmSpLG0O3hxMrsOXjwMeC7wJODdneyUJEmSJElSpd3AzjWjVH0wIj4CPLljPZIkSZIkSWProsdGJkPbgZ1j+DxwGfmVpZKmxHhZD3VLG9vrhvfv29o4bC1zosqfWFa1KaHAS3oBeGJ//66nqrIzVgOzS0bGQJWnsbmsF+TV+rW1/pc+rm4reLh+wt1sL0mSJGk66MTgxR8Av+nAcSRJkiRJUjucebGriLigRfG+wBOBE4C/72SnJEmSJEmSKu3OvHgVuwZ23g/cBrwRuLiTnZIkSZIkSWNw5sWuUkrzJ7gfkiRJkiRJLbX72Mg7gdUppdtb1D0SOCWldG6nOydpLFXw5jp2Dao8vbG9DmYvzx8HcuDmrJ/tD8DgmkUwvzRbX9b9OZzzyGtvAeDWO49g8PD7c10V3NnfW7arfcjBnACLy3pVCelcXuvKklLWX9ZVqCcLGArvHAogrV+jZobdCZ+VJEma4WbQzItZbbZ7F/DoUermlXpJkiRJkqSOazfzItg186LyaGBbZ7ojSZIkSZLGlYAdU92JyTPq4EVEvA54XdlMwEcj4u5Gs/2BpwL/PjHdkyRJkiRJM91YMy/uBbaWzwH8CvhFo81vyQ+jf6TzXZM0trFyIBp5AT210ImSWTF4eMmauHQRLCufV5QcivX5j/6WBUeVcmD2nPx5dflroadkXyxbWw68dPi8fZtL23o/Sg7H+gtr7QE2t+6zZijvA0mSpLYkYOdUd2LyjDp4kVL6HPA5gIj4JPDulNKPJ6tjkiRJkiRJ0P6rUv98ojsiSZIkSZLalJhRbxsZK/PiAuAfUkq3lc9jSSmlMzvbNUmSJEmSpLFnXrwS+DRwW/k8lgQ4eCHtsZ6ybvW8f7Nu6a5NqqyKSn+zfrhs1knbARhk0XDbm/LnYxddB8CN/ceP3H8lsLh8Xl91p+RkVHka/cD8uaNcxoIW/R8rs2Os70OSJEmSMy+KlNJjW32WJEmSJEmaTLOmugOSJEmSJEljGSvz4vm7c6CU0tUPvDuSJEmSJGlcPjYy5IvkryPaOE4C9ulIj6QZqcp2aGQ9zO6FgbWNurLuqfIlNsLqrfnzirkjDzu/rNcz1GZwdbVf2WfJ3KF2Nx5Xsi4GNjbOCawvfaryNTaU4/SX+oG10DfKdbAUuJD2mXUhSZIkadhYgxfmXEiSJEmS1K2ceQEppS2T2RFJkiRJkqRWxpp5sYuIeC7wh8AjgTuAjSmlayeiY5IkSZIkaRRmXuwqIuYBVwJ/APxPWR4BnBsRm4CXppR+OmG9lCRJkiRJM1a7My8uJs+2OC6ldENVGBHPBD4DfBx4Qee7J+3NqlDLpewaULkgrxYDK5YD8IYTLyh1/wjAzSwEYBUruZKXAHAVLwLgND4MwJmnDIdkHpu+DcDGO3Pg5uDKHLg5a9V2BjfMoRysnLeEch5TQj1vmgtV0GcVDjq7bA+Fey4Y7veQ6rp2J6xTuwaeSpIkSQ0zbObFrDbbHQ+cUR+4AEgpfR04C3h2pzsmSZIkSZIE7c+8uBO4b5S6+4C7OtMdSZIkSZI0rgTsmOpOTJ52Z178LTnf4tH1wrL9LuA9ne6YJEmSJEkStD/z4rnAXOBHEfGfDAd2/n75vCQilpS2KaV0Ysd7Kk1bPY3tpY3qudBXsg2WlKyJ/lK3YvjzRRvPGFFXZWCccNkNsLK0X5tXZ64sGRPrSz4FfdzI8fnjmlI0P68GD78SepaXZuUAVXZFT+nPccBAVVcMVPkW61CnmXUhSZKkcSRg51R3YvK0O3hxKLC5LAAHAfcDVQbGwzvcL0mSJEmSJKDNwYuUkoGckiRJkiR1E982IkmSJEmS1B3afWyEiJgHvBB4FLB/sz6ldEYH+yVNM1WORZVvUTILZi8aypZgVVmX7WMXXTe099FsAmAR7wPgpb+5EoA5Pxkcfs/Pw/Jq7aKXA/ARTgMg3RdcsPkNAHyDklFxbV6dxkcAOOGUGzjyklsA2LLiqFy5uurP8uHLqKIWVpTjrN5aq2hmXFSNG9csSZIkaeIlZtTMi7YGLyJiGfApIICfA79tNEmAgxeSJEmSJKnj2p158R7gCuD1KaW7J7A/kiRJkiRJI7Q7eDEX+EcHLiRJkiRJ6gI+NtLS54HFwJcnrivSdLO09nmM3If+sq4yJlbk1bxFtw81uYoXAXDRc/LTV0dem/Mpbj3oiKHTDPbPyR8eVHY6Ka/mX/IDtiwoORZ9JaOiZy4AV6xYPtSHLadUWRf1HIuq7xeO7PPqtaNfzy7MupAkSZI0sdodvHgD8I8RsRq4Dvhls0FK6epOdkySJEmSJI0iATumuhOTp93Bi98B/hB4LHByi/oE7NOpTkmSJEmSJFXaHbz4JHA38CfkOeLNt41IkiRJkqTJkoCdU92JybM7My9ellK6ZiI7I0mSJEmS1NTu4MU3gMdMZEekidcqVHOMoM3R2vT0DldVAZkr5pa6vD72zOsAuOG64ILj3wDAGWsuAmDjstx00cPzuvcEOC0dC8DxZ9+YC0/Iqwu+/AYWcjMAJx/2CQC2XJ+DN49clEM9t5xy1FAI6MvPXAfAFc8pQZ1nlf4tmVsL4VzQuLy5Zm5KkiRJ05FvG9nFm4E1EXEfowd23tvJjkmSJEmSJEH7gxc3l/WnxmhjYKckSZIkSZMh4cyLFk4mfzWSJEmSJEmTqq3Bi5TSmrHqI+JBHemNNKGawQ49Lcpa6Cn5EX0bR5bPB/pL1sWSkVUv4UoA4udpOGJic16d+ekLAZj15O0ADPbM4UhKfsVfHFXOVbIqNs6F1WX/DdVl5H5sWbIob6/fCrNzP65YWfo6UOVbFOvrG+tG1vU1tnNhizJJkiRJXSMBO6a6E5Nn1p7uGNnxEXEJ8LMO9kmSJEmSJGlIu4+NDImIRcCrgVcBhwG/AC7tcL8kSZIkSdJoErBzqjsxedoavIiIp5AHLJaRJ8v/FtiX/BaSD6eUZlBMiCRJkiRJmkyjDl5ExOPIgxWvBp5EzjG9Fngn8FXgJ8B/OXCh7tdT1s0ch75a3dK8KtkRDOTMiVk/259rD3sGAMdvvhGA0xecD8BVvIj+zU8EYPtjRj6BNWfzIABnPPwinrH5yyPqjr32OgDmcTsAj9x8OxdddkauXJVXsxbvD8DgBobyNF5+Sc6xuGJjybVYWTtoM+Ni6FpPL+t1mGMhSZIk7UV828iQPvLXsRH4C+CKlNI2gIg4eBL6JkmSJEmSNGZg5xYggKcAi4FnRMRuZ2RIkiRJkiQ9EKMORqSUHhsRxwKvAV5R1tsi4vPkOehpcrooSZIkSZJ2MYMeG4mUxh+DiIhZwAnk/IuXAA8lD178C/ChlNKmiezkRIiYl+DUqe6GJkUz86KeA9E66+LY9G0AbrzseCgRE5yUV+dfkvdfyM1s4mgAzlxwYetTrwLW54+zVm0HYPC4Obmgiqk4Zi3cVE5yzNaRfe1ZBH1V2brGwavr6WHXPIvRcj4kSZKkmeKcm1NKC6e6FxMl5i5M/PEk/Sr+mZjy77Ktx0BSSoPksM5rI+L1wPPJYZ4vBV4TEf+dUnrixHVTkiRJkiQNScCOqe7E5Bkr86KllNJvU0pfSCktAw4D/ozd+O/diOiJiI9HxLcjYmdEbGjRJiLibRFxa0TcFxH/ERFPb9HuSRHx5Yi4NyJuj4hzI2Kf3b0mSZIkSZLUvR5QAGdKaTvw6bK068nkmRs3AfuO0uYs4B3AW4FbgDcD6yPiKSmlnwFExCHkCfnfB14MPB54P3lAZmWrg0qSJEmStFdIwM6p7sTk2e2ZFx1wVUrpiJTSK4HvNSsjYn/y4MV7U0oXpZTWA68k/2jeUGv6euDBwMtSStemlD4GnAO8OSIOmvCrkCRJkiRJk2LSX31a8jPG8gzgIOCztX22R8RV5HTFalbFUuCalNLdtX0vBc4HngVc1bFOq8u0CqNslvUwFMa5pIRxripV/Xl1z0teN7T3gd/KQ5b3PP0RueA3efXkE79H/8UlzuWM0vi6sjr+2KH97/luflrpw/udNqKnZ995HkeceOuIsi1rj8ofVpeCS5cPh4JWfd1Q1kNhncDs0mhgLSO1emprd4M6DficfvyZSZIkzWiJGfW2kamYeTGeo8iTXzY3yn9Q6urtbqk3SCn9BLi30U6SJEmSJE1jkz7zog2HAPeklJpP72wDDoiIfVNKvy3tftli/22lTpIkSZKkvdMMm3kRKaWpO3nE5cChKaXFtbK3A29JKR3SaHsKcDGwb0ppR0TsKO0+1Gj3U2BNSuntLc53KnAqQC+9R3f6eiRJkiRJ3aGX3ptTSgunuh8TJQ5emHjmpsk52bqY8u+yG2debAMeEhH7NGZfPBS4N6W0o9buoS32P5jWMzJIKV1MHgAhYl4q4xjqSj2M/yx/vU15/n8oF2LjcFmVI7G+ZEWcV9osZjhBZUXj0Mty1sSx6dvceMrxuWxNqTupdsr15XMjT2PouH1rGcreGFL1eV1Zn177vKCsN7doL0mSJElFAnaM22qv0Y2ZF7cA+zCcRldpZlzcQiPbIiKOAOY02kmSJEmSpGmsGx8b2R+4E3hfSmlVKTuA/H/aF6eUVpays4G3AkemlH5dyt4CnAsc3ngLyS7OiXOm7sIlSZIkSRNqr39s5KCFiYWT9NjIV2bgYyNlIOL5ZfNRwEER8YqyfXVK6d6IOA94R0RsI8+ieDN5lsiFtUN9DHgj8PmIOB94HNALfGC8gQuAXj6Oj410Mx8b2bW9JEmSJM1MU5F58Qjgc42yavux5F//ziMPVpwNzAU2Ac9JKd1Z7ZBS2hYRJwAXAVeRcy4+SB7A0LRTPSXUV1tXv/RXv8hX2+UX/fOWDw8eVMogwqz5T+G9h50FwBmbLwJg6xNz3QvPnAfADZtP4PRrzwfgDnLZ5WteC8D89AMAXsKV3H5JrvvEJScDcMJlN+QD9QPzy3mPyYMdrCgDJX1le/ZyGOgtdWW9prQZSgZe17juvUHz5ylJkiSpo2bY20YmffAipdQPxDhtEvCesozV7vvA8R3rnCRJkiRJ6jrd+LYRSZIkSZI0lhk286Ib3zYiSZIkSZI0xJkX6hJjZSNUWRclr3V2b16vBvo25s9LFg2XAayCWzkCgPjP/GKZWT/dDsDgxjm5fPWuL5yJDSXM85S8OnPxhbAhfz5h4Kpy/tJ4YC1DAZuzq/OX/lS5HAMwlP+wugSGzogciJlwjZIkSZImi4MXkiRJkiRNNwnYMdWdmDw+NiJJkiRJkrqaMy8kSZIkSZpuErBzqjsxeZx5IUmSJEmS9lhEnBQRqcXy+k6dw5kXmiA97BraWAVvbmYo6LIKs5w9N6/nl+IVDIVvzro+B23e/dB3A7Bxv38H4Dl3XstLD9sMwOWbjwHgugXHDp3t+DU3AnDVSS8CYBUrAXjtysvzcVdtZ3BlDu888pJbANhy2VF5+8SyfcpRsLgccEnpYxUKynLoLx8Hekdez9C1wlB4pyRJkiR1Uve9KvV44L7a9o87dWAHLyRJkiRJUid8M6V0z0Qc2MELSZIkSZKmm0Q3zryYMGZeSJIkSZKkTvhRRAxExA8j4i86eWBnXqjDqqyHes5DT6OsZ9ey+cvzenEpPmsrrMgZE4Nrci7FgatzlO6Rm3MexeBxc7hibd4vli8f2Y35DOdn/PlaAF7bk7Mu6Nua998wF/py3ZbVS0d0dcuyTWXnag2srzI8qr73MTLbos6cC0mSJEkTKAE7proTQ+4A3gF8A9gHeDXwsYg4IKX0wU6cwMELSZIkSZI0lkMjovY/u1ycUrq42kgpXQNcU6tfFxH7ASsj4kMppcEH2gEHLyRJkiRJmm4SsHPSznZXSmnhbu5zOfAq8pz4B/zWETMvJEmSJEnSREmdOIgzL/QA1fMfgNmL8nqgB2bPLZ83AjDrZy8FclYFq3LVsSfOK/tfB8A8bgfgtEs+wsl8YkTZ7WfmtltOOSrvMh+OXJTzL9icV1ueU+pWAcfk8x5ZBgi3xNbSj/1zPw7fOtxvLiyX0ciwmL0cBnob19xX+9zMtuhDkiRJkiZc979t5OXAXcCWThzMwQtJkiRJkrTHIuIKcljnd8iBnSeW5Y2dyLsABy8kSZIkSZqeumfmxQ+Bk4EjgAC+D/xZSumfO3UCBy8kSZIkSdIeSym9DXjbRJ4jUupIdsa0EzEvwalT3Y1pqoddsh1m9+b1wNpSsAB6Sv5FfylaXGtflc1vrOtWlPXqap0zK7i0ZGmsr7Wt6oZyNra2OGDJteD0su5jKCxj6Hp6GMkMC0mSJGl6OufmPXhDxrQR+y5MHLpp/IadcEdM+XfpzAtJkiRJkqabBOyY6k5MHl+VKkmSJEmSupozLyRJkiRJmm4SsHOqOzF5nHkhSZIkSZK6mjMvZrwqoLJVYOWCsq5CLZfm1Yq5Q81mnbQdgMHjSpP5ywF4+bVruWJjDuz88qJnALCJowE485QLmbW27LdyDgBHXnILALfeeUQuf/QcZq0qbfpzG5aUMM755VwrgGM2ls8lHHT1xkafgZ7l5RLrQZ0A69iVAZ2aqZp/F0iSJKmrJbrpVakTzpkXkiRJkiSpqznzQpIkSZKk6caZF5IkSZIkSd0jUkpT3YcpETEvwalT3Y0uUuVBXMiuz76XuhUlc2L11uH8iQ2lyeKyXlHW84HV5XN/o27Z1uHTnleOc9bWxjnWwuySVTGwNq+XlO311f7r2PX5/KW1Op/hlyRJkmauc25OKS2c6l5MlIiFidmbJudkAzHl36UzLyRJkiRJUlcz80KSJEmSpOlo51R3YPI480KSJEmSJHU1Z17MCPUciErPyLrZJWvi+t7hHIv1eTVr7XYABlfm7Tekf+QqXgTA937zZACes9+1ANzOPAC2nHLU8CnKetbicpwlc2FVqSvH5NJy/mUbS3+WD3d1djPron5d1TVVuRb1azTrQpIkSdJebAZFWDrzQpIkSZIkdTUHLyRJkiRJUldz8EKSJEmSJHU1My/2alXoxObG9lKG8iCqrIuBkidxTB+sWJQ/z8+rwePmjNi+KP43rMj7HbhmlHjbga0M5U+UzIrBs64crl9ffSj9WH/66PubXSFJkiRJM5ozLyRJkiRJUldz8EKSJEmSJHU1By8kSZIkaWvTPgAAH/FJREFUSVJXc/BCkiRJkiR1NQM7p716CGdVVEI4+9aOqDs2fRuAGxfMHQrcvOf/7APAgd/KwZvnL+rl7DufAsDgyhLU2Z9Xb9h8AQAXnX/GcIbmSXk1a9X2kfv0zIX1OaizCvqkv2xvqHV/cVmv39q4nnb0YJinWqvuI+8PSZIk7a0SsGOqOzFpnHkhSZIkSZK6mjMvJEmSJEmadhIwMNWdmDTOvJAkSZIkSV0tUkpT3YcpETEvwalT3Y1xNJ/bXwqsa7Q5vdGmlgOxYlFerylV15f1cQxlVbCkrFeO0Y2+rSO3e+YO51isL7kas0uexcDGFgfY3KJsQVk3r0eSJEmSOuGcm1NKC6e6FxMl4vcSfHWSznbwlH+XzryQJEmSJEldzcwLSZIkSZKmHTMvJEmSJEmSuoYzL7pST2N7aSleBP0lx2JxqZpf1hvm5vUqmLV4fwAG1+Sie+7ZB4Ar93tpLtgB87gdgOfceW1uu2rO8PH6yzFXl/XacuySi3Hktbew5UFH5Y0VJetidZWLUfreMxf6mvkXVc5FqwyMZr6HNJP550GSJEnjScCOqe7EpHHmhSRJkiRJ6mrOvJAkSZIkadpx5oUkSZIkSVLXcOZF16jlXMwuORIDVY7Eurzq2wxLSt2SUjWUS5FX5y86nTM3Xjii7sDVO/OH+Xk1a+12BleWjIvVZcfquOu3wuy5jfOX7Q15tSU2weySebG65FrMLlkcA72lr83cjro+dn2W32f7pWH+eZAkSZLqHLyQJEmSJGla8lWpkiRJkiRJXcGZF5IkSZIkTTsGdk65iNgQEWmU5djSpr9F3c+muu+SJEmSJKmzunXmxWnAQY2yc4HfA75ZK/sX4MLa9m8nuF8dsLSx3Qi2XDF3KIxz1uL9ATjtsNsB+Midf8Wiw64b0fzoMzcBsIhvAPDS31zJmeUrecPmCwC4mYUA3Pig4wEYXD5nKHxzKKhzftk+by6sL5/nl6DONS2uYWDjyP5XQZ0jGMopSZIkSRMjMZMyL7py8CKl9P36dkTsCywELksp1X86d6SUbprUzkmSJEmSpEnVlYMXLTwPOAT4zFR3RJIkSZKkqWfmRTdaBvwU+Fqj/OSI+G1E/CoiLo+II6egb5IkSZIkaQJFSmmq+zCmiDgAuBO4OKX0N7XyDwE3AbcBTwTeBewEnppS+tX4x52X4NSJ6fQuqlyLPuD0kVWzS67E4rK9AVibP85avH1E08E1c2B1daittWMCKxYN71/V9ZRj95V8ip5Fw/sO1TWOs0t/69Y16lu1kSRJkqRucM7NKaWFU92LiRLxpDT0y+OEO3rKv8vp8NjIC4EDaTwyklL669rm1yLiBuBbwJ8Df9/qQBFxKkMjFgdPQFclSZIkSVKnTYfBi2VAX0pp01iNUkrfjYgfAr8/RpuLgYuhmnkhSZIkSdJ0ZOZF14iIg8nvFt2doE4HJSRJkiRJ2ot0+8yLlwL70cbgRUQ8BXgC8PHOd6OH4WyHZh5Etb2U4UyIUrekd+RhlsCsk3KOxaLDcg7FjaccD8A/X/KKXc56O48E4FaOyAVnwhFn3po/XnZhLusv2RXz82rWqu0MrmnkaCxfNKIN/XNhVVVX2lYvoO2ZC33Vc1MLyrq6roo5F9LUGy2bRpIkSTNDYvgXub1ftw9eLAO+nVL6Qb0wIv4EWA58EbgdOApYCfwEWDPJfZQkSZIkSROoawcvIuJQ4ATgHS2qbwUeQQ7mfCiwFfgS8LaU0t2T1klJkiRJkqbEzMq86NrBi5TSXcCDRqn7DnlgQ5IkSZIk7eW6dvCiu7R6prynsb0OepaPLKpyJY4bLho867sA3HjT8SOavvb8y/OHszbCipJRsXprXi8puRQbgIFStqLsuKas55fj990P3J83Zld5FmWfviq7YgEs21w+N66tb9QNSV3FP5+SJEmaORy8kCRJkiRp2plZgZ1d/apUSZIkSZIkZ15IkiRJkjTtzKzATmdeSJIkSZKkrubMi7Y0wzmB2Y1wzuvJgZoA6/MqbQ8A1u54OQAf4TRuZx4AC1mbG5Vszis25uMdm7YD1wFw44Yc6vmGay8A4KIFZ/DyzTl084rLyvnXlHP29eb1it7hoM+BtaNcz7oW11Tf7muUGQwoSZIkSd3HzAtJkiRJkqSu4MwLSZIkSZKmHTMvJEmSJEmSukaklKa6D1MiYl6CU0epbZFxMWTByM3Zi4Y/D2zM6/MWjWxTMjCYz3AuxqpGXX+LU63f2ujW3OHPVcZFT+/IbZYCm6tGLQ4qSZIkSTPBOTenlBZOdS8mSsTjE/ztJJ1t2ZR/l868kCRJkiRJXc3MC0mSJEmSpp2EbxuRJEmSJEnqEjN45sX+5GyLei5EybqYvXxk04Gtw3kTi0vZisbhNgCrc9bFsWdeB8CN5x+f69aXLIxLF8HqkmMxvxyv2l5RtvsZzr+oytaU7b6Nw+eb3VvKypqlZb1u1+sx+0KSJEmS9jK+bUSSJEmSJKlrzOCZF5IkSZIkTVdmXkiSJEmSJHWNSClNdR+mRMQTUw6T2FwrrbIhqqyIBXk1exEMrB27DesYzp3oqZXV91naom5BY1uSJEmS9MCdc3NKaeFU92KiRMxPsHKSznbKlH+XPjYiSZIkSdK0Y2CnJEmSJElS13DmhSRJkiRJ046BnZIkSZIkSV1jBs+8+BU5JLMevFkFbl5Y1iVoc2AzI0M366qgzR7GD92s1zeDO6vtvhZt6mWSJEmSJJl5IUmSJEmS1DUcvJAkSZIkadqpMi8mYxlfRDwpIr4cEfdGxO0RcW5E7NORS2VGPzYiSZIkSZIeqIg4BFgPfB94MfB44P3kCRMrO3EOBy+G8iTGypWo142Wa9Fq/7EyK5pl7bSRJEmSJAm6LPPi9cCDgZellO4Gro2Ig4DeiLiglD0gPjYiSZIkSZIeiKXANY1BikvJAxrP6sQJHLyQJEmSJGna6arMi6OAW0b0LqWfAPeWugfMwQtJkiRJkvRAHAL8skX5tlL3gM3gzIs77oJztkx1L6bAocBdU90JaTd532q68t7VdOW9q+nI+1ZNR051BybWHddA76GTdLL9I2JTbfvilNLFjTapxX4xSvlum7GDFymlh091H6ZCRGxKKS2c6n5Iu8P7VtOV966mK+9dTUfet5ppUkrPm+o+1GwDHtqi/GBaz8jYbT42IkmSJEmSHohbaGRbRMQRwBwaWRh7ysELSZIkSZL0QKwD/jgiHlIrOxG4D/hqJ07g4MXM03wuSZoOvG81XXnvarry3tV05H0rTZ2PAb8BPh8RSyLiVKAX+EDj9al7LFLqSHaGJEmSJEmaoSLiScBFwLHknIvVQG9KaWdHju/ghSRJkiRJ6mY+NtKFIuIVEXFDRGyNiPsj4ocRsTIi9q21iYh4W0TcGhH3RcR/RMTTWxzrSRHx5Yi4NyJuj4hzI2KfRpuOHUuqRMSjIuKeiEgRcWCt3HtXXSUiTir3aXN5fa2N9626UkTMjoizImJzRPwmIm6LiA822nj/qqtExIZR/t5NEXFsaeN9K2kEBy+601zgK8AKYCnwCeDtwAdqbc4C3gGcD7wQuAdYHxGHVw0i4hBgPfm9ui8GzgX+Bjincb5OHkuqvI98LzV576pbHU+e5lgtn6/Ved+qW30SeCPwd8BzyffXfY023r/qNqcx8u/bY4FrgbuAb5Y23reSRkopuUyDBXgP+bmhAPYHfgW8s1Y/B/g5sKpWdjb5fbsH1crOAO6tyjp5LBeXagH+CPgF8BbyPwIOLOXeuy5dtwAn1e/TFvXety5duQDPA3YATxqjjfevS9cvwL7l3w0fLdvety4uLrsszryYPraS/2IHeAZwEPDZqjKltB24ijxTo7IUuCaNTHe9FHgw8KwJOJZEmV55Ifl/LO5qVHvvajryvlW3Ohm4LqX0/THaeP9qOngecAjwmbLtfStpFw5edLGI2CciDoiI48hTQj+aUkrAUcBOYHNjlx+UuspRwC31Bimln5BHkI+qtenUsSSA15P/l+PDLeq8d9XNfhQRA5Fzhv6iVu59q261CPjviLgoIu4uz+l/PiLm1dp4/2o6WAb8FPha2fa+lbQLBy+62/ayfA34KvDWUn4IcE/a9ZUz24ADYjjY8xDyoyZN20pdp4+lGS4i5gLvBt6cUtrRoon3rrrRHeRnoV9LfhZ6I/CxiHhTqfe+Vbc6nPzY09PJv/z9OXA0cGVERGnj/auuFhEHkP/uvaz8Jx1430pqYfZUd0BjegZwAPCHwDvJ78w9rdS1esdttKgbrV07bfbkWJrZ3gNsTCldPUYb7111lZTSNcA1taJ1EbEfsDIiPlQ1a7Gr962mWpTlxSmlrQARcQf5PzyOB75c2nn/qpu9EDiQ4UdGKt63kkZw8KKLpZT+s3y8PiLuAj4VEe8njwA/JCL2aYwiPxS4t/Y/3ttKWdPBDI8sd/JYmsEi4snk56//V0RU98oBZX1wROzEe1fTx+XAq4D5eN+qe20DflwNXBTXA78FnkQevPD+VbdbBvSllDbVyrxvJe3Cx0amj2og47Hk5/H2AXoabZrP6t1C4xm9iDiCnLB8S61Np46lmW0B8CDgRvI/ALYxnHtxGznE03tX003C+1bd6wejlAcwWD57/6prRcTB5KDM5qwL71tJu3DwYvp4Zln/P+AG4G7glVVl7XnBdbV91gF/HBEPqZWdSH7/+1fLdiePpZnteuDZjeX8Uvd84H1472r6eDn5bTlb8L5V9/oi8LsRcWit7H+RB5K/Xba9f9XNXgrsx66DF963knY11e9qddl1Ab4EvIU8Ev1c4BzgHuDSWpuzyenHfwWcAPwb+R/ah9XaHEIOorsWWAKcWo6zqnG+jh3LxaW+kIPkEnBgrcx716WrFuAK4Mzyd+4LgH8u9+3ptTbety5dt5Bf//gT8oy3FwKvAW4Frm208/516cqF/G/eb41S533r4uIyYpnyDri0+KHktzV8t/yF+UvyIyOnAw+qtQng7eTp+PeR30jyey2O9STgutLmjnLsfRptOnYsF5f6QuvBC+9dl65agL8Fflj+YXsfcDPw2kYb71uXrlzIU+GvJr+dbBuwBjik0cb716XrFuBQYAdw1ij13rcuLi4jlkjJ8FxJkiRJktS9zLyQJEmSJEldzcELSZIkSZLU1Ry8kCRJkiRJXc3BC0mSJEmS1NUcvJAkSZIkSV3NwQtJkiRJktTVHLyQNKNFRG9E3LUH+70qIk6agC51REScFBEpIg7swLGeGBFfi4jt5ZjzR2nXX+pTRPw2Im6JiHdExL67eb49+pnsrnKeqr9fmOjzTTcRMSsiPhwRd5bvqHcPj3NqRLykw93riIh4ZERcHRG/Kte4eJz2jy3tfhIRMUa7V0XEzyKr32eDEbEtIr4ZEe+JiMM7flFdLiLOGO973oNjnlT7jjd18tiSpO4xe6o7IEnT1KuAQ4E1U9yPyfA+4KHAi4DtwB1jtP0X4EJgP+DZwLuAg4G3THAf99SvgOcBv5jqjnShlwGnAf8b+D5w2x4e51Tgu0A3DhC9HXga8GryPfD9cdq/uqyPAJ4JXD9Kuz8Brk4ppTLGUd1nkP88/D7wl8CpEfG8lNLNe3wF088ZwEXAhg4e89+AY4F3AId18LiSpC7i4IUkTRMR8eCU0n1TcOqjgH9NKX25jbZ3pJRuKp+/GhGPBl4fEW9NKaWJ6+IeG6j1d9qYpHvhKGBbSukTE3yeqXQUsDGldHWb7V8N3AQ8tXzeZfAiImaRBypOqxU377NrIuKjwH8Al0XEE1JKO/fkAqaLibhnI+JBwGBK6efAzyPi5zh4IUl7LR8bkaSaiFhcTR+PiM9FxD0R8eOIOK3WZg3wcuBZtanKvbX6F0fEpoi4v0wdv6D8I7t+nldGxOaIuC8ivhIRv1eOc1KtTX9EvL88enEbcHcpPzYi/jUibi+PcnwrIv50D6/36RHx5Yi4t0xn/3REHFbq5kdEAh4PvKn0b8NunuJmYA5waHW8iHhBow9rxprqHREPioi/K1P1f1Ou+8r64ygR8ZiIuDQiflGu5ZqIeMJu9rU61qMj4rMR8T/l5/OjiHh3o81pEXFr+f6viojn1B87aPdaI+Ko0u9bS7+/FxH/p/wCXLWp7sk/Lj/3e8j/c1092nFWRPSV7+a/I+J1bVzjARHxD+X+vL88xvDcWv0G4N3AIbV7fP4ox3pyRHypfPfbI+IHEfFXteMcDbyudpyTavuuKNf8m4jYEhFntPq+IuIlkR9Duj8iro+IJ7VxjY+NiC9ExN0R8evyc+qp1SfgBOClpV/94xzvycBTgE8C/wq8MiJa/SfQHwCHANeOdbyU0i/JsxAeDzxnnHMfGRGfiYi7yn3ynYh4Ta3+0Ij4VERsLfUbImJh4xj95c/RmyLitvLn/dKIeGipn1N+fqe1OP+miPjn2vaYf95q9/+fRsQ/RcQvgavKdzwXeFftflhc9hn3Xi7XdXnkR5F+BNwPzBvru5Mk7T2ceSFJrV0CfAq4mPw/rB+OiE0ppW+Qf6l7DPlRiuof+rdBftYd+AzwceBt5F9M3kseLH5LabMQuBS4HDgdeCJw2Sj9eA3wvXKe6u/sI4GvAx8j/+P9mcAnI2IwpfSZdi8wIh5Onrr9g3KeA4HzgGtLH+8gT8W+EriO/DjI3e0ev5gP/JY8JX/Obu5bORv4U+As4P8BhwPPB/Yp1/Ew8v+AbwVeD9xb2q6PiN/Zg//t/SfgweTHHX4JPI78P/SU870Y+DD5+/8C8CxgT2cnPAr4IfBp4NfA04Fzyvnf22j7j+RfnP+e/HOH/DN5HXAu8J/kX4I/ERFbU0pfHOO8l5AfA3ob0AecAvxbRDw7pXQ9+X57M/AKhh93GO1xoX8FbgGWA78BngAcVOpOA64Afkz+cwPwI4CIeCvwt8AF5PvwaODdEXFvSumi2vGPBD5AfiTgvvL9XBMRC1JK99NCROwHfBnYUa5toOz31Yh4akrpF+R7+yPkn/HbSt/H8ppynCvKd/Fq8uDHNY12fwJ8LaXUzp+Vr5RjHgN8aZRreQRwI/m+fgtwK3kQ5Yhasy8APaX+LuCtwFci4vdSSn21dq8CvkO+tx9N/l7/FjgtpbQ9Ir4InEj+XqrzP478s+kt27vz5+3vgM8DrwR2AtvKNV8OrC5tqkd12r2Xn0n+e/XMcu5ftfreJEl7oZSSi4uLy4xdyP8gv6u2vRhIwLm1sgcBPwfOq5VdDmxoHCuALcAnG+Unk3/pmlu2P0fOAIhamzPKeU+qlfWTf0naf4z+B3lQ4+PAdbXyk8rxDhxj3/PIv7gdVCv7w7Lfqxv9+Ls2vst+4P2lPwcALyD/YnF5qZ9fjv2Cxn5rgE1j/Ey+CLx/jPO+m/yL1MNqZYeUc/9Vuz/7Wvk9wAvH2O8bwLpG2SXl2hbvzrWO8rN8G/DjFvfkBxvte4BB4HWN8n8CvjlG/5/Y3I88uPZd4Jrxvp/GsQ4tfXvqGG02AWsaZQeV7/ldjfJzgZ8B+9S+rwQ8o9bmSPIv/K8f45yvL20eVyt7NHkg7exa2Ybq/mzj/v4ROccCYF/ygNyaFu1uBt7c7vdI/jP+0THq30vOmnnkKPXPK9/Rs2plc8h/Z328VtZfrmF2rez/b+9cY+2oyjD8fECwIlCxSCkakB8SEUQuUhW8FRCUglBbQAhBjdoEQUkAFS8hxYCmJYESBBEjHsAABUMtpIKFctUiIXKJIi0Y8ajQ0kLDRRBbw+ePd23OnOneu3t2j+0pfZ9k0jOzZ9asNbPW7l7v+i6zgWWV/SlIZNipcuzbpa1b9jreKv1/bpv6PgvM6Kcvl/f1b2DHDs9igA7jy5s3b968bfyb3UaMMaY9C1p/ZOZq4Ak0+enGbsgi4/qI2KK1IauFMWi1FGRWfnNmVmNA3NShzIVZW12OiO2Kyf8gWllejVZSd+utaa8zEViQlRXilGXJ34CPNCyrxemlPi8DNyOf/lP6LKvFw8AXQlkK9opYI8vDIchE/8XKM38JTSI/QHMeBn4YymCwc/WDiNgc2AeYV7vmxj7uQ0SMiYhzIuIvaOV/NXAesGsbl4T5tf2D0YRvbq2/LQT2LnVtx/5IKLmhdSAzXyv7Td/7SmQJcFlEHFesBHrhw2iCfUObsTKe4WNteWYuqtR1EL3biV3Knwg8mJl/rVz3T2Sx1LhvR8QHkQXOdaWsVeidT4mIMZXzJqD+UX9XXYtfy+cHAbdmZifLl4nAisy8u3UgM19Gol+9rXdm5n8r+38GdoghF6xbkKh0TOWc45AIsarsNxlvvT6HJn35D5m5rMdyjTHGvIGweGGMMe15vra/CgkQ3di+/PtrhkSF1cjVAYbMvHdEq6JV6vstnmlzbABNKM4HDkWT0St6qF+dCR3KfwZ4W8OyWvyi1GcvZNFxZGa2u0cTzkVuGl8FHgH+ERGnVT7fHj2P1bVtEsNN63vlOGQtcCEwGIopcnD57O3IOmJ57Zr6fq/MRKb+lyNXmP1Re2HN91l/jtsj15kXGN7ugVLHCR3uOQH4V2a+0qb8rYrLRU8U0eNQZC1xBbAslFZ3n7Vc2horj9bqfmc5Xn1v7Z7tcjq3D0a+bx9f6ndXRLy1xImYjyxIDq+cdziymlnSS6FF+BjXoa4txtE9w0+Ttrb7XgtkSUIRSuehMUCJY/F+imhTaDLeeh37Tfryun6fGGOM2UhxzAtjjBk5Wuk2pwMPtfm8JWIsQ5PgKvX9FsMydJTJzmTg1My8rHK8HzF6KdBupXw8WkXth2cys1PwzZYFyZa1410nk2VCdTZwdkS8G7kEzI6IJZl5K3ruNzEUU6HKSz3XfOh+TyFLj83QqvYM4KZihbECuSPUn1t9v9e2HgNcnJmzWgciYnKnqtX2V5a6HIhWret0ElSWAltHxFY1AWM88Epmri32w/BKZS4GpoaC0n4UCTLzI+KdRdxoR2usHEH7yWh18t+uj+6AhI9OLAX2aHN8PA3T4pZ+cCxyHxtsc8rxDFneTKaZ1cUk9Fvsvi7nPEd3oabbOO4nBfAcFFxzZyRSrEAWMS2ajLdeMww16cujMWuRMcaY9YDFC2OM6Y92lhhLgKeAd2XmT7tc+wBwZER8p+I68pke7/smtEL5+gQzIrYp1zf9UX8/cHJEbJOZL5Wy9kf+6mukgBwBlqPV1N1bByJia+RC0G5SuAaZ+UREnIlcUd6LghwuRJPLR3MEUzGWiffvI+IcYBGwS2Y+FxEPA0ehgJ0tPlu7vNe2vpnh73Jz4HM9VvEO1BfGZmbXzBY1HkB9ZRqKKUBxxZnGOrz34l51R0RcAFyDAtqupP1YuQ/FLtgpM9c22d8hIg5ouY6USfW+KHhpJ+4HToqIXTPzyXLdO4ADKIEnG/AJJB58C8U7qfJF4NgyBv+DXCqm9VJosd6YiQKm3t7l1IXA1yNifAcrpvuBcyLiY5l5Tyl7KySkzO2lLjUWoMCaxyLx4pc5PI3ruo63dv2h375sjDFmE8LihTHG9Mdi4KiIOBplGnk6M5+OiDOAqyNiW+Q/vgr5yh8NTCsr3TPRhOO6iPg5muB+pZTbaaUagMx8ISIeQFYIL5bzz0Lm1tt2u7YNFwAno8wNMxnKNvJHlFFhRMnM1yJiHkq7OohM2M9Ak9iORMRcZAnyUDl3Gvr/655KO05EE+eLkYA0HmUB+W02y8AyFmWPuAp4HIlFZyBrmcfKaT8AboyIH6PJ4ccZysjRtK23AaeUmBcrkSjTk9tGZi6JiMtQP5qFXF3GIIuD3TLzyx2ueywirgV+VPppK9vIe1B/6JmI2AtllJiDMopshyb5j6QyeoDGymERcRiyIniyiEAzgIsiYhf0LjdDcVsmZeaUym2eRWOqlW3k+0gcGuhStYFSj1si4mwUhHJGKesnTdqILCueBy6qW6WUMXgSGt9LSxvuXqME2CIiPlT+3gZl7zgZBbb9VE0cqHNhuce9EXEeijGyO/CWzJyVmb+JiN8BcyLiLPSMz0TC2PkN20pmri5j7nQk2tRTp67reFsMTI6IW1F8jSX99mVjjDGbFo55YYwx/XEpWqG8Aq1kTwfIzDloVX5vFADxRvTj/0EkZFDcKo5HE5hfAVMZmjT2kl7xBOSCchVwERIarmragMxcgczWX0XpXS8B7gU+WQnON9KcioImXlrudy3DTdLbsQhNDq9B/vj7AVNb7imZ+SxKNbkYTfQWoPSbY1FayCa8isSb05Bp/JUoHeOhrVXmzJyLUtweid7fPsCX+mzr19AzvwT1pT+xZorUbpyCzPdPQrFWBtCK+z1drgGJFVei9KPzUAaPI1JpUpuwDLl9fBeJdZcikadqSXRuOXY9xeoIoLjKTAc+XepwLUqJe2/tHoMo9ecMFHvhReCweiDbKkVkOAT1iZ+Vtg6ibDA9u1IUV5ipwA3t3Gky80EU9PIE9Nxv7+B2MxZZmyxC3wvTUHyY92VmVxetMk4PROLdbBSIczrw98ppU5AQNruUH8BBOTxNahOuQ8LF09TexwiMt2+ggL7zUX/Yrxzvty8bY4zZRIjhwe6NMcZsCCLiROBqlNrxybWdb9adsvJ/Kgqg+lqX+Ay9lLUnEj0mZeZdI1JBQ0QMAHtmZj9ZY9YrEfE4cP5aXMbM/4Hi9rQ5Eqr22Bj6izHGmObYbcQYYzYAxeXgNuRbvi/wPWC+hYv1zjgUm2Iesu4wpi8ys2mqYjNyfJ6hGCj9Bhs2xhgzyrF4YYwxG4ZxyMR+HPJRnwN8c4PWaNPjcmSCDxKRjDEbJzejNMMglxRjjDFvQOw2YowxxhhjjDHGmFGNA3YaY4wxxhhjjDFmVGPxwhhjjDHGGGOMMaMaixfGGGOMMcYYY4wZ1Vi8MMYYY4wxxhhjzKjG4oUxxhhjjDHGGGNGNRYvjDHGGGOMMcYYM6r5H+dGyxH9aQaqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            \n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            x.append(maximum)\n",
    "            #y.append(suma/maximum)\n",
    "            y.append(suma)\n",
    "            z.append((maximum))\n",
    "            \n",
    "\n",
    "def Cut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "\n",
    "                    if (maximum <= maxmax) and (maximum >= minmin):\n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "     \n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(1,figsize=(20,10))\n",
    "matplotlib.rc('axes',edgecolor='black')\n",
    "\n",
    "#ble = plt.hist2d(y, x, bins=(500,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "ble = plt.hist2d(y, x, bins=(500,128),  cmap=plt.cm.jet)\n",
    "#plt.xlim(60000,125000)\n",
    "#plt.ylim(50,225)\n",
    "plt.title('CANDY')\n",
    "plt.xlabel('Integral of Pulse [square of step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "cb = plt.colorbar()\n",
    "\n",
    "maxmax1 = 100\n",
    "maxmax2 = 100\n",
    "maxmax3 = 100\n",
    "maxmax4 = 46\n",
    "maxmax5 = 35\n",
    "width = 1\n",
    "\n",
    "minmin1 = maxmax1-width\n",
    "plt.plot([0,150000],[maxmax1,maxmax1],c='r',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin1,minmin1],c='r',lw=1,zorder=99)\n",
    "\n",
    "minmin2 = maxmax2-width\n",
    "plt.plot([0,150000],[maxmax2,maxmax2],c='g',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin2,minmin2],c='g',lw=1,zorder=99)\n",
    "\n",
    "minmin3 = maxmax3-width\n",
    "plt.plot([0,150000],[maxmax3,maxmax3],c='purple',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin3,minmin3],c='purple',lw=1,zorder=99)\n",
    "\n",
    "minmin4 = maxmax4-width\n",
    "plt.plot([0,150000],[maxmax4,maxmax4],c='orange',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin4,minmin4],c='orange',lw=1,zorder=99)\n",
    "\n",
    "minmin5 = maxmax5-width\n",
    "plt.plot([0,150000],[maxmax5,maxmax5],c='grey',lw=1,zorder=99)\n",
    "plt.plot([0,150000],[minmin5,minmin5],c='grey',lw=1,zorder=99)\n",
    "\n",
    "plt.figure(2,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "Cut(maxmax1,minmin1,'r')\n",
    "Cut(maxmax2,minmin2,'g')\n",
    "Cut(maxmax3,minmin3,'purple')\n",
    "Cut(maxmax4,minmin4,'orange')\n",
    "Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data has no positive values, and therefore can not be log-scaled.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-ccc0108c4e8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Integral of Pulse [square of step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Amplitude of Pulse [step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0mmaxmax1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m190\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(mappable, cax, ax, **kw)\u001b[0m\n\u001b[1;32m   2235\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m \u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar_doc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(self, mappable, cax, ax, use_gridspec, **kw)\u001b[0m\n\u001b[1;32m   1593\u001b[0m                 \u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m         \u001b[0mcax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1595\u001b[0;31m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_ax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mcolorbar_factory\u001b[0;34m(cax, mappable, **kwargs)\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mColorbarPatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1330\u001b[0;31m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mColorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1332\u001b[0m     \u001b[0mcid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacksSM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'changed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_mappable_changed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, mappable, **kw)\u001b[0m\n\u001b[1;32m    903\u001b[0m                 \u001b[0mkw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m             \u001b[0mColorbarBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_mappable_changed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, cmap, norm, alpha, values, boundaries, orientation, ticklocation, extend, spacing, ticks, format, drawedges, filled, extendfrac, extendrect, label)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0;31m# The rest is in a method so we can recalculate when clim changes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_extend_lower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_config_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_solids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_config_axes\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m    442\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mupdate_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \"\"\"\n\u001b[1;32m    372\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m         \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ticker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    374\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_ticker\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mintv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 594\u001b[0;31m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    595\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogLocator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m             \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1536\u001b[0m         \u001b[0;34m'Return the locations of the ticks'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1538\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1556\u001b[0;31m                     \u001b[0;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1557\u001b[0m                     \"log-scaled.\")\n\u001b[1;32m   1558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function post_execute at 0x7f9fa8f44848> (for post_execute):\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m                     \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/_pylab_helpers.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2024\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2026\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2028\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2178\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2180\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2181\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2182\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 527\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    528\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9e7fbb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            \n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            x.append(maximum)\n",
    "            #y.append(suma/maximum)\n",
    "            y.append(suma)\n",
    "            z.append((maximum))\n",
    "            \n",
    "\n",
    "def Cut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    suma = 0\n",
    "                    colour = 'black'\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "                        suma = suma + np_data[i]\n",
    "                        \n",
    "                    if (maximum <= maxmax) and (maximum >= minmin):\n",
    "                        if (suma>0) and (suma<73000):\n",
    "                            colour = 'red'\n",
    "                        if (suma>73000) and (suma<82000):\n",
    "                            colour = 'green'\n",
    "                        if (suma>82000) and (suma<100000):\n",
    "                            colour = 'purple'\n",
    "                        if (suma>100000) and (suma<150000):\n",
    "                            colour = 'orange'\n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "     \n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(1,figsize=(20,10))\n",
    "matplotlib.rc('axes',edgecolor='black')\n",
    "plt.xlim(42000,175000)\n",
    "plt.ylim(35,130)\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "plt.title('CANDY x CERF')\n",
    "plt.xlabel('Integral of Pulse [square of step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "cb = plt.colorbar()\n",
    "\n",
    "maxmax1 = 190\n",
    "maxmax2 = 167\n",
    "maxmax3 = 123\n",
    "maxmax4 = 105\n",
    "maxmax5 = 60\n",
    "width = 4\n",
    "\n",
    "minmin1 = maxmax1-width\n",
    "#plt.plot([0,175000],[maxmax1,maxmax1],c='r',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin1,minmin1],c='r',lw=1,zorder=99)\n",
    "\n",
    "minmin2 = maxmax2-width\n",
    "#plt.plot([0,175000],[maxmax2,maxmax2],c='g',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin2,minmin2],c='g',lw=1,zorder=99)\n",
    "\n",
    "minmin3 = maxmax3-width\n",
    "plt.plot([0,175000],[maxmax3,maxmax3],c='purple',lw=1,zorder=99)\n",
    "plt.plot([0,175000],[minmin3,minmin3],c='purple',lw=1,zorder=99)\n",
    "\n",
    "minmin4 = maxmax4-width\n",
    "#plt.plot([0,175000],[maxmax4,maxmax4],c='orange',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin4,minmin4],c='orange',lw=1,zorder=99)\n",
    "\n",
    "minmin5 = maxmax5-width\n",
    "#plt.plot([0,175000],[maxmax5,maxmax5],c='grey',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin5,minmin5],c='grey',lw=1,zorder=99)\n",
    "\n",
    "plt.figure(2,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "#Cut(maxmax1,minmin1,'r')\n",
    "#Cut(maxmax2,minmin2,'g')\n",
    "Cut(maxmax3,minmin3,'purple')\n",
    "#Cut(maxmax4,minmin4,'orange')\n",
    "#Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vertical Cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data has no positive values, and therefore can not be log-scaled.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-61752287803a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Integral of Pulse [square of step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Amplitude of Pulse [step of A/D converter]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0mmaxmax1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m81000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mcolorbar\u001b[0;34m(mappable, cax, ax, **kw)\u001b[0m\n\u001b[1;32m   2235\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmappable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m \u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar_doc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36mupdate_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \"\"\"\n\u001b[1;32m    372\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m         \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ticker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    374\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_ticker\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mintv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 594\u001b[0;31m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    595\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogLocator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    596\u001b[0m             \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1536\u001b[0m         \u001b[0;34m'Return the locations of the ticks'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1538\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mtick_values\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1556\u001b[0;31m                     \u001b[0;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1557\u001b[0m                     \"log-scaled.\")\n\u001b[1;32m   1558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function post_execute at 0x7f9fa8f44848> (for post_execute):\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m                     \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/_pylab_helpers.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2024\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2026\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2028\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0mdsu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mitemgetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m             \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2323\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2324\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2326\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m                               \" correctly with this backend.\")\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_image_magnification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "values must all be positive",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
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      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2323\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2324\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2326\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m                               \" correctly with this backend.\")\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_image_magnification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, magnification)\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyintv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             self._get_unsampled_image(self._A, [_x1, _x2, _y1, _y2],\n\u001b[0;32m--> 624\u001b[0;31m                                       transformed_viewLim)\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_get_unsampled_image\u001b[0;34m(self, A, image_extents, viewlim)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgbacache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m                     \u001b[0;31m# Avoid side effects: to_rgba can return its argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                     \u001b[0;31m# unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/cm.pyc\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(self, x, alpha, bytes)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;31m# This is the normal case, mapping a scalar array:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/matplotlib/colors.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"minvalue must be less than or equal to maxvalue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"values must all be positive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: values must all be positive"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9e7c9510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            \n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            x.append(maximum)\n",
    "            #y.append(suma/maximum)\n",
    "            y.append(suma)\n",
    "            z.append((maximum))\n",
    "            \n",
    "\n",
    "def Cut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    suma = 0\n",
    "                    colour = 'black'\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "                        suma = suma + np_data[i]\n",
    "                        \n",
    "                    if (maximum <= maxmax) and (maximum >= minmin):\n",
    "                        if (suma>0) and (suma<73000):\n",
    "                            colour = 'purple'\n",
    "                        if (suma>73000) and (suma<82000):\n",
    "                            print suma, \".\"\n",
    "                            colour = 'red'\n",
    "                        if (suma>82000) and (suma<100000):\n",
    "                            print suma\n",
    "                            colour = 'green'\n",
    "                        if (suma>100000) and (suma<150000):\n",
    "                            colour = 'orange'\n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "def VCut(maxmax,minmin,colour):\n",
    "    for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "        try:\n",
    "            with h5py.File(f,'r') as hf:\n",
    "                frames = np.array(hf.get('FRAMES'))\n",
    "                for n in range(1,frames+1):\n",
    "                    data = hf.get(str(n))\n",
    "                    np_data = 1.0 * np.array(data)\n",
    "\n",
    "                    maximum = 0\n",
    "                    minimum = 255\n",
    "                    suma = 0\n",
    "                    for i in range(1400):\n",
    "                        if np_data[i] > maximum:\n",
    "                            maximum = np_data[i]\n",
    "                        if np_data[i] < minimum:\n",
    "                            minimum = np_data[i]\n",
    "                        suma = suma + np_data[i]\n",
    "                        \n",
    "                    if (suma<=maxmax) and (suma>=minmin):\n",
    "                        #print maximum\n",
    "                        colour = 'green'\n",
    "                        if (maximum>70):\n",
    "                            colour = 'red'\n",
    "                            \n",
    "                        plt.plot(np_data, c=colour)\n",
    "        except:        \n",
    "            pass\n",
    "\n",
    "     \n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "    \n",
    "plt.figure(1,figsize=(20,10))\n",
    "matplotlib.rc('axes',edgecolor='black')\n",
    "plt.xlim(42000,175000)\n",
    "plt.ylim(35,130)\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,128), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "plt.title('Protons X Photons')\n",
    "plt.xlabel('Integral of Pulse [square of step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "cb = plt.colorbar()\n",
    "\n",
    "maxmax1 = 81000\n",
    "maxmax2 = 167\n",
    "maxmax3 = 80\n",
    "maxmax4 = 105\n",
    "maxmax5 = 60\n",
    "width = 1\n",
    "\n",
    "minmin1 = maxmax1-1000\n",
    "plt.plot([maxmax1,maxmax1],[0,250],c='r',lw=1,zorder=99)\n",
    "plt.plot([minmin1,minmin1],[0,250],c='r',lw=1,zorder=99)\n",
    "\n",
    "minmin2 = maxmax2-width\n",
    "#plt.plot([0,175000],[maxmax2,maxmax2],c='g',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin2,minmin2],c='g',lw=1,zorder=99)\n",
    "\n",
    "minmin3 = maxmax3-width\n",
    "plt.plot([0,175000],[maxmax3,maxmax3],c='purple',lw=1,zorder=99)\n",
    "plt.plot([0,175000],[minmin3,minmin3],c='purple',lw=1,zorder=99)\n",
    "\n",
    "minmin4 = maxmax4-width\n",
    "#plt.plot([0,175000],[maxmax4,maxmax4],c='orange',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin4,minmin4],c='orange',lw=1,zorder=99)\n",
    "\n",
    "minmin5 = maxmax5-width\n",
    "#plt.plot([0,175000],[maxmax5,maxmax5],c='grey',lw=1,zorder=99)\n",
    "#plt.plot([0,175000],[minmin5,minmin5],c='grey',lw=1,zorder=99)\n",
    "\n",
    "plt.figure(2,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "#Cut(maxmax1,minmin1,'r')\n",
    "#Cut(maxmax2,minmin2,'g')\n",
    "Cut(maxmax3,minmin3,'purple')\n",
    "#Cut(maxmax4,minmin4,'orange')\n",
    "#Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.figure(3,figsize=(7,7))\n",
    "#matplotlib.rc('axes',edgecolor='r')\n",
    "#Cut(maxmax1,minmin1,'r')\n",
    "#Cut(maxmax2,minmin2,'g')\n",
    "VCut(maxmax1,minmin1,'purple')\n",
    "#Cut(maxmax4,minmin4,'orange')\n",
    "#Cut(maxmax5,minmin5,'grey')\n",
    "\n",
    "plt.xlabel('Sample Number ['+ str(xinc*1e9)+' ns per sample]')\n",
    "plt.ylabel('Amplitude [step of A/D converter, '+ str(yinc*1e3) + ' mV per step]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limited maximum histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/data40_1538561816-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data22_1538560556-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data15_1538559989-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data09_1538559561-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data14_1538559914-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data10_1538559633-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data23_1538560624-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data18_1538560275-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data13_1538559846-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data37_1538561604-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data32_1538561255-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data11_1538559705-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data42_1538561956-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data31_1538561188-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data30_1538561116-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data02_1538559068-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data01_1538559000-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data28_1538560976-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data03_1538559141-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data16_1538560062-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data41_1538561888-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data46_1538562237-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data38_1538561676-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data34_1538561398-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data29_1538561048-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data33_1538561325-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data08_1538559493-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data45_1538562166-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data05_1538559281-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data07_1538559421-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data43_1538562028-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data25_1538560764-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data06_1538559353-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data27_1538560908-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data19_1538560347-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data39_1538561748-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data21_1538560484-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data20_1538560415-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data17_1538560206-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data44_1538562098-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data36_1538561537-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data12_1538559774-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data26_1538560835-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data35_1538561465-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data04_1538559209-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data24_1538560696-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n",
      "./data/data47_1538562305-CHAN1.h5 XINC: 5e-09  YINC: 0.0008  FRAMES: 508  YORIGIN: -87.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fd70f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fd70310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' YORIGIN:', yorigin \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 151):\n",
    "                x.append(maximum)\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append((maximum))\n",
    "     \n",
    "for f in glob.iglob(\"./data/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./data/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print f,\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,150), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram for oscilloscope B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fcb58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f9fcb5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "all_frames = 0\n",
    "xinc = 0\n",
    "yinc = 0\n",
    "\n",
    "def ProcessData():\n",
    "        global all_frames\n",
    "        global xinc\n",
    "        global yinc\n",
    "        #print('List of arrays in this file: \\n', hf.keys())\n",
    "        xinc = np.array(hf.get('XINC'))\n",
    "        yinc = np.array(hf.get('YINC'))\n",
    "        yorigin = np.array(hf.get('YORIGIN'))\n",
    "        xorigin = np.array(hf.get('XORIGIN'))\n",
    "        trig = np.array(hf.get('TRIG'))\n",
    "        capturing = np.array(hf.get('CAPTURING'))\n",
    "        \n",
    "        frames = np.array(hf.get('FRAMES'))\n",
    "        all_frames = all_frames + frames\n",
    "        print 'XINC:', xinc, ' YINC:', yinc, ' FRAMES:', frames, ' XORIGIN:', xorigin, ' YORIGIN:', yorigin, ' TRIG:', trig        \n",
    "        print 'CAPTURING:', capturing         \n",
    "        \n",
    "        for n in range(1,frames+1):\n",
    "            data = hf.get(str(n))\n",
    "            #np_data = np.array(data)\n",
    "            np_data = np.array(data)\n",
    "            #np_data += *np.array(hf.get('YORIGIN'))\n",
    "            #np_data = np.array(hf.get('YINC'))*np_data\n",
    "            #plt.plot(np_data)\n",
    "            #print np_data\n",
    "            maximum = 0\n",
    "            minimum = 255\n",
    "            suma = 0\n",
    "            for i in range(1400):\n",
    "                if np_data[i] > maximum:\n",
    "                    maximum = np_data[i]\n",
    "                if np_data[i] < minimum:\n",
    "                    minimum = np_data[i]\n",
    "                suma = suma + np_data[i]\n",
    "            if (maximum < 256):\n",
    "            #if (maximum < 254):\n",
    "                x.append(maximum)\n",
    "                #y.append(suma/maximum)\n",
    "                y.append(suma)\n",
    "                z.append((maximum))\n",
    "     \n",
    "for f in glob.iglob(\"./dataB/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "for f in glob.iglob(\"./dataB/**/data*.h5\"): # generator, search immediate subdirectories \n",
    "    print '  ', f\n",
    "    try:\n",
    "        with h5py.File(f,'r') as hf:\n",
    "                ProcessData()\n",
    "    except:        \n",
    "        pass\n",
    "\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "\n",
    "\n",
    "ble = plt.hist2d(y, x, bins=(500,129), norm=matplotlib.colors.LogNorm(), cmap=plt.cm.jet)\n",
    "#ble = plt.hist2d(y, x, bins=(500,200),  cmap=plt.cm.jet)\n",
    "plt.xlabel('Integral of Pulse [square of the step of A/D converter]')\n",
    "plt.ylabel('Amplitude of Pulse [step of A/D converter]')\n",
    "#cb = plt.colorbar()\n",
    "#cb.set_label('Count')\n",
    "\n",
    "#plt.plot([0,10000],[100,100],ls='-',c='r',marker='o')\n",
    "#plt.plot([0,100000],[100,1000],c='r',lw=3,zorder=99)\n",
    "#plt.xlim(60000,160000)\n",
    "#plt.ylim(50,150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}